{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from argparse import Namespace\n",
    "from collections import Counter\n",
    "import json\n",
    "import re\n",
    "import string\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm import tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Vocabulary(object):\n",
    "    \"\"\"Class to process text and extract vocabulary for mapping\"\"\"\n",
    "\n",
    "    def __init__(self, token_to_idx=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            token_to_idx (dict): a pre-existing map of tokens to indices\n",
    "        \"\"\"\n",
    "\n",
    "        if token_to_idx is None:\n",
    "            token_to_idx = {}\n",
    "        self._token_to_idx = token_to_idx\n",
    "\n",
    "        self._idx_to_token = {idx: token \n",
    "                              for token, idx in self._token_to_idx.items()}\n",
    "        \n",
    "    def to_serializable(self):\n",
    "        \"\"\" returns a dictionary that can be serialized \"\"\"\n",
    "        return {'token_to_idx': self._token_to_idx}\n",
    "\n",
    "    @classmethod\n",
    "    def from_serializable(cls, contents):\n",
    "        \"\"\" instantiates the Vocabulary from a serialized dictionary \"\"\"\n",
    "        return cls(**contents)\n",
    "\n",
    "    def add_token(self, token):\n",
    "        \"\"\"Update mapping dicts based on the token.\n",
    "\n",
    "        Args:\n",
    "            token (str): the item to add into the Vocabulary\n",
    "        Returns:\n",
    "            index (int): the integer corresponding to the token\n",
    "        \"\"\"\n",
    "        if token in self._token_to_idx:\n",
    "            index = self._token_to_idx[token]\n",
    "        else:\n",
    "            index = len(self._token_to_idx)\n",
    "            self._token_to_idx[token] = index\n",
    "            self._idx_to_token[index] = token\n",
    "        return index\n",
    "            \n",
    "    def add_many(self, tokens):\n",
    "        \"\"\"Add a list of tokens into the Vocabulary\n",
    "        \n",
    "        Args:\n",
    "            tokens (list): a list of string tokens\n",
    "        Returns:\n",
    "            indices (list): a list of indices corresponding to the tokens\n",
    "        \"\"\"\n",
    "        return [self.add_token(token) for token in tokens]\n",
    "\n",
    "    def lookup_token(self, token):\n",
    "        \"\"\"Retrieve the index associated with the token \n",
    "        \n",
    "        Args:\n",
    "            token (str): the token to look up \n",
    "        Returns:\n",
    "            index (int): the index corresponding to the token\n",
    "        \"\"\"\n",
    "        return self._token_to_idx[token]\n",
    "\n",
    "    def lookup_index(self, index):\n",
    "        \"\"\"Return the token associated with the index\n",
    "        \n",
    "        Args: \n",
    "            index (int): the index to look up\n",
    "        Returns:\n",
    "            token (str): the token corresponding to the index\n",
    "        Raises:\n",
    "            KeyError: if the index is not in the Vocabulary\n",
    "        \"\"\"\n",
    "        if index not in self._idx_to_token:\n",
    "            raise KeyError(\"the index (%d) is not in the Vocabulary\" % index)\n",
    "        return self._idx_to_token[index]\n",
    "\n",
    "    def __str__(self):\n",
    "        return \"<Vocabulary(size=%d)>\" % len(self)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self._token_to_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SequenceVocabulary(Vocabulary):\n",
    "    def __init__(self, token_to_idx=None, unk_token=\"<UNK>\",\n",
    "                 mask_token=\"<MASK>\", begin_seq_token=\"<BEGIN>\",\n",
    "                 end_seq_token=\"<END>\"):\n",
    "\n",
    "        super(SequenceVocabulary, self).__init__(token_to_idx)\n",
    "\n",
    "        self._mask_token = mask_token\n",
    "        self._unk_token = unk_token\n",
    "        self._begin_seq_token = begin_seq_token\n",
    "        self._end_seq_token = end_seq_token\n",
    "\n",
    "        self.mask_index = self.add_token(self._mask_token)\n",
    "        self.unk_index = self.add_token(self._unk_token)\n",
    "        self.begin_seq_index = self.add_token(self._begin_seq_token)\n",
    "        self.end_seq_index = self.add_token(self._end_seq_token)\n",
    "\n",
    "    def to_serializable(self):\n",
    "        contents = super(SequenceVocabulary, self).to_serializable()\n",
    "        contents.update({'unk_token': self._unk_token,\n",
    "                         'mask_token': self._mask_token,\n",
    "                         'begin_seq_token': self._begin_seq_token,\n",
    "                         'end_seq_token': self._end_seq_token})\n",
    "        return contents\n",
    "\n",
    "    def lookup_token(self, token):\n",
    "        \"\"\"Retrieve the index associated with the token \n",
    "          or the UNK index if token isn't present.\n",
    "        \n",
    "        Args:\n",
    "            token (str): the token to look up \n",
    "        Returns:\n",
    "            index (int): the index corresponding to the token\n",
    "        Notes:\n",
    "            `unk_index` needs to be >=0 (having been added into the Vocabulary) \n",
    "              for the UNK functionality \n",
    "        \"\"\"\n",
    "        if self.unk_index >= 0:\n",
    "            return self._token_to_idx.get(token, self.unk_index)\n",
    "        else:\n",
    "            return self._token_to_idx[token]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NMTVectorizer(object):\n",
    "    \"\"\" The Vectorizer which coordinates the Vocabularies and puts them to use\"\"\"        \n",
    "    def __init__(self, source_vocab, target_vocab, max_source_length, max_target_length):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            source_vocab (SequenceVocabulary): maps source words to integers\n",
    "            target_vocab (SequenceVocabulary): maps target words to integers\n",
    "            max_source_length (int): the longest sequence in the source dataset\n",
    "            max_target_length (int): the longest sequence in the target dataset\n",
    "        \"\"\"\n",
    "        self.source_vocab = source_vocab\n",
    "        self.target_vocab = target_vocab\n",
    "        \n",
    "        self.max_source_length = max_source_length\n",
    "        self.max_target_length = max_target_length\n",
    "        \n",
    "\n",
    "    def _vectorize(self, indices, vector_length=-1, mask_index=0):\n",
    "        \"\"\"Vectorize the provided indices\n",
    "        \n",
    "        Args:\n",
    "            indices (list): a list of integers that represent a sequence\n",
    "            vector_length (int): an argument for forcing the length of index vector\n",
    "            mask_index (int): the mask_index to use; almost always 0\n",
    "        \"\"\"\n",
    "        if vector_length < 0:\n",
    "            vector_length = len(indices)\n",
    "        \n",
    "        vector = np.zeros(vector_length, dtype=np.int64)\n",
    "        vector[:len(indices)] = indices\n",
    "        vector[len(indices):] = mask_index\n",
    "\n",
    "        return vector\n",
    "    \n",
    "    def _get_source_indices(self, text):\n",
    "        \"\"\"Return the vectorized source text\n",
    "        \n",
    "        Args:\n",
    "            text (str): the source text; tokens should be separated by spaces\n",
    "        Returns:\n",
    "            indices (list): list of integers representing the text\n",
    "        \"\"\"\n",
    "        indices = [self.source_vocab.begin_seq_index]\n",
    "        indices.extend(self.source_vocab.lookup_token(token) for token in text.split(\" \"))\n",
    "        indices.append(self.source_vocab.end_seq_index)\n",
    "        return indices\n",
    "    \n",
    "    def _get_target_indices(self, text):\n",
    "        \"\"\"Return the vectorized source text\n",
    "        \n",
    "        Args:\n",
    "            text (str): the source text; tokens should be separated by spaces\n",
    "        Returns:\n",
    "            a tuple: (x_indices, y_indices)\n",
    "                x_indices (list): list of integers representing the observations in target decoder \n",
    "                y_indices (list): list of integers representing predictions in target decoder\n",
    "        \"\"\"\n",
    "        indices = [self.target_vocab.lookup_token(token) for token in text.split(\" \")]\n",
    "        x_indices = [self.target_vocab.begin_seq_index] + indices\n",
    "        y_indices = indices + [self.target_vocab.end_seq_index]\n",
    "        return x_indices, y_indices\n",
    "        \n",
    "    def vectorize(self, source_text, target_text, use_dataset_max_lengths=True):\n",
    "        \"\"\"Return the vectorized source and target text\n",
    "        \n",
    "        The vetorized source text is just the a single vector.\n",
    "        The vectorized target text is split into two vectors in a similar style to \n",
    "            the surname modeling in Chapter 7.\n",
    "        At each timestep, the first vector is the observation and the second vector is the target. \n",
    "        \n",
    "        \n",
    "        Args:\n",
    "            source_text (str): text from the source language\n",
    "            target_text (str): text from the target language\n",
    "            use_dataset_max_lengths (bool): whether to use the global max vector lengths\n",
    "        Returns:\n",
    "            The vectorized data point as a dictionary with the keys: \n",
    "                source_vector, target_x_vector, target_y_vector, source_length\n",
    "        \"\"\"\n",
    "        source_vector_length = -1\n",
    "        target_vector_length = -1\n",
    "        \n",
    "        if use_dataset_max_lengths:\n",
    "            source_vector_length = self.max_source_length + 2\n",
    "            target_vector_length = self.max_target_length + 1\n",
    "            \n",
    "        source_indices = self._get_source_indices(source_text)\n",
    "        source_vector = self._vectorize(source_indices, \n",
    "                                        vector_length=source_vector_length, \n",
    "                                        mask_index=self.source_vocab.mask_index)\n",
    "        \n",
    "        target_x_indices, target_y_indices = self._get_target_indices(target_text)\n",
    "        target_x_vector = self._vectorize(target_x_indices,\n",
    "                                        vector_length=target_vector_length,\n",
    "                                        mask_index=self.target_vocab.mask_index)\n",
    "        target_y_vector = self._vectorize(target_y_indices,\n",
    "                                        vector_length=target_vector_length,\n",
    "                                        mask_index=self.target_vocab.mask_index)\n",
    "        return {\"source_vector\": source_vector, \n",
    "                \"target_x_vector\": target_x_vector, \n",
    "                \"target_y_vector\": target_y_vector, \n",
    "                \"source_length\": len(source_indices)}\n",
    "        \n",
    "    @classmethod\n",
    "    def from_dataframe(cls, bitext_df):\n",
    "        \"\"\"Instantiate the vectorizer from the dataset dataframe\n",
    "        \n",
    "        Args:\n",
    "            bitext_df (pandas.DataFrame): the parallel text dataset\n",
    "        Returns:\n",
    "            an instance of the NMTVectorizer\n",
    "        \"\"\"\n",
    "        source_vocab = SequenceVocabulary()\n",
    "        target_vocab = SequenceVocabulary()\n",
    "        \n",
    "        max_source_length = 0\n",
    "        max_target_length = 0\n",
    "\n",
    "        for _, row in bitext_df.iterrows():\n",
    "            source_tokens = row[\"source_language\"].split(\" \")\n",
    "            if len(source_tokens) > max_source_length:\n",
    "                max_source_length = len(source_tokens)\n",
    "            for token in source_tokens:\n",
    "                source_vocab.add_token(token)\n",
    "            \n",
    "            target_tokens = row[\"target_language\"].split(\" \")\n",
    "            if len(target_tokens) > max_target_length:\n",
    "                max_target_length = len(target_tokens)\n",
    "            for token in target_tokens:\n",
    "                target_vocab.add_token(token)\n",
    "            \n",
    "        return cls(source_vocab, target_vocab, max_source_length, max_target_length)\n",
    "\n",
    "    @classmethod\n",
    "    def from_serializable(cls, contents):\n",
    "        source_vocab = SequenceVocabulary.from_serializable(contents[\"source_vocab\"])\n",
    "        target_vocab = SequenceVocabulary.from_serializable(contents[\"target_vocab\"])\n",
    "        \n",
    "        return cls(source_vocab=source_vocab, \n",
    "                   target_vocab=target_vocab, \n",
    "                   max_source_length=contents[\"max_source_length\"], \n",
    "                   max_target_length=contents[\"max_target_length\"])\n",
    "\n",
    "    def to_serializable(self):\n",
    "        return {\"source_vocab\": self.source_vocab.to_serializable(), \n",
    "                \"target_vocab\": self.target_vocab.to_serializable(), \n",
    "                \"max_source_length\": self.max_source_length,\n",
    "                \"max_target_length\": self.max_target_length}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NMTDataset(Dataset):\n",
    "    def __init__(self, text_df, vectorizer):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            surname_df (pandas.DataFrame): the dataset\n",
    "            vectorizer (SurnameVectorizer): vectorizer instatiated from dataset\n",
    "        \"\"\"\n",
    "        self.text_df = text_df\n",
    "        self._vectorizer = vectorizer\n",
    "\n",
    "        self.train_df = self.text_df[self.text_df.split=='train']\n",
    "        self.train_size = len(self.train_df)\n",
    "\n",
    "        self.val_df = self.text_df[self.text_df.split=='val']\n",
    "        self.validation_size = len(self.val_df)\n",
    "\n",
    "        self.test_df = self.text_df[self.text_df.split=='test']\n",
    "        self.test_size = len(self.test_df)\n",
    "\n",
    "        self._lookup_dict = {'train': (self.train_df, self.train_size),\n",
    "                             'val': (self.val_df, self.validation_size),\n",
    "                             'test': (self.test_df, self.test_size)}\n",
    "\n",
    "        self.set_split('train')\n",
    "\n",
    "    @classmethod\n",
    "    def load_dataset_and_make_vectorizer(cls, dataset_csv):\n",
    "        \"\"\"Load dataset and make a new vectorizer from scratch\n",
    "        \n",
    "        Args:\n",
    "            surname_csv (str): location of the dataset\n",
    "        Returns:\n",
    "            an instance of SurnameDataset\n",
    "        \"\"\"\n",
    "        text_df = pd.read_csv(dataset_csv)\n",
    "        train_subset = text_df[text_df.split=='train']\n",
    "        return cls(text_df, NMTVectorizer.from_dataframe(train_subset))\n",
    "\n",
    "    @classmethod\n",
    "    def load_dataset_and_load_vectorizer(cls, dataset_csv, vectorizer_filepath):\n",
    "        \"\"\"Load dataset and the corresponding vectorizer. \n",
    "        Used in the case in the vectorizer has been cached for re-use\n",
    "        \n",
    "        Args:\n",
    "            surname_csv (str): location of the dataset\n",
    "            vectorizer_filepath (str): location of the saved vectorizer\n",
    "        Returns:\n",
    "            an instance of SurnameDataset\n",
    "        \"\"\"\n",
    "        text_df = pd.read_csv(dataset_csv)\n",
    "        vectorizer = cls.load_vectorizer_only(vectorizer_filepath)\n",
    "        return cls(text_df, vectorizer)\n",
    "\n",
    "    @staticmethod\n",
    "    def load_vectorizer_only(vectorizer_filepath):\n",
    "        \"\"\"a static method for loading the vectorizer from file\n",
    "        \n",
    "        Args:\n",
    "            vectorizer_filepath (str): the location of the serialized vectorizer\n",
    "        Returns:\n",
    "            an instance of SurnameVectorizer\n",
    "        \"\"\"\n",
    "        with open(vectorizer_filepath) as fp:\n",
    "            return NMTVectorizer.from_serializable(json.load(fp))\n",
    "\n",
    "    def save_vectorizer(self, vectorizer_filepath):\n",
    "        \"\"\"saves the vectorizer to disk using json\n",
    "        \n",
    "        Args:\n",
    "            vectorizer_filepath (str): the location to save the vectorizer\n",
    "        \"\"\"\n",
    "        with open(vectorizer_filepath, \"w\") as fp:\n",
    "            json.dump(self._vectorizer.to_serializable(), fp)\n",
    "\n",
    "    def get_vectorizer(self):\n",
    "        \"\"\" returns the vectorizer \"\"\"\n",
    "        return self._vectorizer\n",
    "\n",
    "    def set_split(self, split=\"train\"):\n",
    "        self._target_split = split\n",
    "        self._target_df, self._target_size = self._lookup_dict[split]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._target_size\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"the primary entry point method for PyTorch datasets\n",
    "        \n",
    "        Args:\n",
    "            index (int): the index to the data point \n",
    "        Returns:\n",
    "            a dictionary holding the data point: (x_data, y_target, class_index)\n",
    "        \"\"\"\n",
    "        row = self._target_df.iloc[index]\n",
    "\n",
    "        vector_dict = self._vectorizer.vectorize(row.source_language, row.target_language)\n",
    "\n",
    "        return {\"x_source\": vector_dict[\"source_vector\"], \n",
    "                \"x_target\": vector_dict[\"target_x_vector\"],\n",
    "                \"y_target\": vector_dict[\"target_y_vector\"], \n",
    "                \"x_source_length\": vector_dict[\"source_length\"]}\n",
    "        \n",
    "    def get_num_batches(self, batch_size):\n",
    "        \"\"\"Given a batch size, return the number of batches in the dataset\n",
    "        \n",
    "        Args:\n",
    "            batch_size (int)\n",
    "        Returns:\n",
    "            number of batches in the dataset\n",
    "        \"\"\"\n",
    "        return len(self) // batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_nmt_batches(dataset, batch_size, shuffle=True, \n",
    "                            drop_last=True, device=\"cpu\"):\n",
    "    \"\"\"A generator function which wraps the PyTorch DataLoader.  The NMT Version \"\"\"\n",
    "    dataloader = DataLoader(dataset=dataset, batch_size=batch_size,\n",
    "                            shuffle=shuffle, drop_last=drop_last)\n",
    "\n",
    "    for data_dict in dataloader:\n",
    "        lengths = data_dict['x_source_length'].numpy()\n",
    "        sorted_length_indices = lengths.argsort()[::-1].tolist()\n",
    "        \n",
    "        out_data_dict = {}\n",
    "        for name, tensor in data_dict.items():\n",
    "            out_data_dict[name] = data_dict[name][sorted_length_indices].to(device)\n",
    "        yield out_data_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Machine Translation Model\n",
    "\n",
    "Components:\n",
    "\n",
    "1. NMTEncoder\n",
    "    - accepts as input a source sequence to be embedded and fed through a bi-directional GRU\n",
    "2. NMTDecoder\n",
    "    - using the encoder state and attention, the decoder generates a new sequence\n",
    "    - the ground truth target sequence is used as input to the decoder at each time step\n",
    "    - an alternative formulation would allow some of the decoder's own choices to be used as input\n",
    "    - this is referred to as curriculum learning, learning to search\n",
    "        - TODO: Look up references for this.  I believe Bengio has a paper from the image captioning competitions. Hal Daume has tons on this and is the main NLP guy for it. \n",
    "3. NMTModel\n",
    "    - Combines the encoder and decoder into a single class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NMTEncoder(nn.Module):\n",
    "    def __init__(self, num_embeddings, embedding_size, rnn_hidden_size):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            num_embeddings (int): number of embeddings is the size of source vocabulary\n",
    "            embedding_size (int): size of the embedding vectors\n",
    "            rnn_hidden_size (int): size of the RNN hidden state vectors \n",
    "        \"\"\"\n",
    "        super(NMTEncoder, self).__init__()\n",
    "    \n",
    "        self.source_embedding = nn.Embedding(num_embeddings, embedding_size, padding_idx=0)\n",
    "        self.birnn = nn.GRU(embedding_size, rnn_hidden_size, bidirectional=True, batch_first=True)\n",
    "    \n",
    "    def forward(self, x_source, x_lengths):\n",
    "        \"\"\"The forward pass of the model\n",
    "        \n",
    "        Args:\n",
    "            x_source (torch.Tensor): the input data tensor.\n",
    "                x_source.shape is (batch, seq_size)\n",
    "            x_lengths (torch.Tensor): a vector of lengths for each item in the batch\n",
    "        Returns:\n",
    "            a tuple: x_unpacked (torch.Tensor), x_birnn_h (torch.Tensor)\n",
    "                x_unpacked.shape = (batch, seq_size, rnn_hidden_size * 2)\n",
    "                x_birnn_h.shape = (batch, rnn_hidden_size * 2)\n",
    "        \"\"\"\n",
    "        x_embedded = self.source_embedding(x_source)\n",
    "        # create PackedSequence; x_packed.data.shape=(number_items, embeddign_size)\n",
    "        x_packed = pack_padded_sequence(x_embedded, x_lengths.detach().cpu().numpy(), \n",
    "                                        batch_first=True)\n",
    "        \n",
    "        # x_birnn_h.shape = (num_rnn, batch_size, feature_size)\n",
    "        x_birnn_out, x_birnn_h  = self.birnn(x_packed)\n",
    "        # permute to (batch_size, num_rnn, feature_size)\n",
    "        x_birnn_h = x_birnn_h.permute(1, 0, 2)\n",
    "        \n",
    "        # flatten features; reshape to (batch_size, num_rnn * feature_size)\n",
    "        #  (recall: -1 takes the remaining positions, \n",
    "        #           flattening the two RNN hidden vectors into 1)\n",
    "        x_birnn_h = x_birnn_h.contiguous().view(x_birnn_h.size(0), -1)\n",
    "        \n",
    "        x_unpacked, _ = pad_packed_sequence(x_birnn_out, batch_first=True)\n",
    "        \n",
    "        return x_unpacked, x_birnn_h\n",
    "\n",
    "def verbose_attention(encoder_state_vectors, query_vector):\n",
    "    \"\"\"A descriptive version of the neural attention mechanism \n",
    "    \n",
    "    Args:\n",
    "        encoder_state_vectors (torch.Tensor): 3dim tensor from bi-GRU in encoder\n",
    "        query_vector (torch.Tensor): hidden state in decoder GRU\n",
    "    Returns:\n",
    "        \n",
    "    \"\"\"\n",
    "    batch_size, num_vectors, vector_size = encoder_state_vectors.size()\n",
    "    vector_scores = torch.sum(encoder_state_vectors * query_vector.view(batch_size, 1, vector_size), \n",
    "                              dim=2)\n",
    "    vector_probabilities = F.softmax(vector_scores, dim=1)\n",
    "    weighted_vectors = encoder_state_vectors * vector_probabilities.view(batch_size, num_vectors, 1)\n",
    "    context_vectors = torch.sum(weighted_vectors, dim=1)\n",
    "    return context_vectors, vector_probabilities, vector_scores\n",
    "\n",
    "def terse_attention(encoder_state_vectors, query_vector):\n",
    "    \"\"\"A shorter and more optimized version of the neural attention mechanism\n",
    "    \n",
    "    Args:\n",
    "        encoder_state_vectors (torch.Tensor): 3dim tensor from bi-GRU in encoder\n",
    "        query_vector (torch.Tensor): hidden state\n",
    "    \"\"\"\n",
    "    vector_scores = torch.matmul(encoder_state_vectors, query_vector.unsqueeze(dim=2)).squeeze()\n",
    "    vector_probabilities = F.softmax(vector_scores, dim=-1)\n",
    "    context_vectors = torch.matmul(encoder_state_vectors.transpose(-2, -1), \n",
    "                                   vector_probabilities.unsqueeze(dim=2)).squeeze()\n",
    "    return context_vectors, vector_probabilities\n",
    "\n",
    "\n",
    "class NMTDecoder(nn.Module):\n",
    "    def __init__(self, num_embeddings, embedding_size, rnn_hidden_size, bos_index):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            num_embeddings (int): number of embeddings is also the number of \n",
    "                unique words in target vocabulary \n",
    "            embedding_size (int): the embedding vector size\n",
    "            rnn_hidden_size (int): size of the hidden rnn state\n",
    "            bos_index(int): begin-of-sequence index\n",
    "        \"\"\"\n",
    "        super(NMTDecoder, self).__init__()\n",
    "        self._rnn_hidden_size = rnn_hidden_size\n",
    "        self.target_embedding = nn.Embedding(num_embeddings=num_embeddings, \n",
    "                                             embedding_dim=embedding_size, \n",
    "                                             padding_idx=0)\n",
    "        self.gru_cell = nn.GRUCell(embedding_size + rnn_hidden_size, \n",
    "                                   rnn_hidden_size)\n",
    "        self.hidden_map = nn.Linear(rnn_hidden_size, rnn_hidden_size)\n",
    "        self.classifier = nn.Linear(rnn_hidden_size * 2, num_embeddings)\n",
    "        self.bos_index = bos_index\n",
    "    \n",
    "    def _init_indices(self, batch_size):\n",
    "        \"\"\" return the BEGIN-OF-SEQUENCE index vector \"\"\"\n",
    "        return torch.ones(batch_size, dtype=torch.int64) * self.bos_index\n",
    "    \n",
    "    def _init_context_vectors(self, batch_size):\n",
    "        \"\"\" return a zeros vector for initializing the context \"\"\"\n",
    "        return torch.zeros(batch_size, self._rnn_hidden_size)\n",
    "            \n",
    "    def forward(self, encoder_state, initial_hidden_state, target_sequence):\n",
    "        \"\"\"The forward pass of the model\n",
    "        \n",
    "        Args:\n",
    "            encoder_state (torch.Tensor): the output of the NMTEncoder\n",
    "            initial_hidden_state (torch.Tensor): The last hidden state in the  NMTEncoder\n",
    "            target_sequence (torch.Tensor): the target text data tensor\n",
    "        Returns:\n",
    "            output_vectors (torch.Tensor): prediction vectors at each output step\n",
    "        \"\"\"    \n",
    "        # We are making an assumption there: The batch is on first\n",
    "        # The input is (Batch, Seq)\n",
    "        # We want to iterate over sequence so we permute it to (S, B)\n",
    "        target_sequence = target_sequence.permute(1, 0)\n",
    "        output_sequence_size = target_sequence.size(0)\n",
    "\n",
    "        # use the provided encoder hidden state as the initial hidden state\n",
    "        h_t = self.hidden_map(initial_hidden_state)\n",
    "\n",
    "        batch_size = encoder_state.size(0)\n",
    "        # initialize context vectors to zeros\n",
    "        context_vectors = self._init_context_vectors(batch_size)\n",
    "        # initialize first y_t word as BOS\n",
    "        y_t_index = self._init_indices(batch_size)\n",
    "        \n",
    "        h_t = h_t.to(encoder_state.device)\n",
    "        y_t_index = y_t_index.to(encoder_state.device)\n",
    "        context_vectors = context_vectors.to(encoder_state.device)\n",
    "\n",
    "        output_vectors = []\n",
    "        self._cached_p_attn = []\n",
    "        self._cached_ht = []\n",
    "        self._cached_decoder_state = encoder_state.cpu().detach().numpy()\n",
    "        \n",
    "        for i in range(output_sequence_size):\n",
    "            y_t_index = target_sequence[i]\n",
    "                \n",
    "            # Step 1: Embed word and concat with previous context\n",
    "            y_input_vector = self.target_embedding(y_t_index)\n",
    "            rnn_input = torch.cat([y_input_vector, context_vectors], dim=1)\n",
    "            \n",
    "            # Step 2: Make a GRU step, getting a new hidden vector\n",
    "            h_t = self.gru_cell(rnn_input, h_t)\n",
    "            self._cached_ht.append(h_t.cpu().detach().numpy())\n",
    "            \n",
    "            # Step 3: Use the current hidden to attend to the encoder state\n",
    "            context_vectors, p_attn, _ = verbose_attention(encoder_state_vectors=encoder_state, \n",
    "                                                           query_vector=h_t)\n",
    "            \n",
    "            # auxillary: cache the attention probabilities for visualization\n",
    "            self._cached_p_attn.append(p_attn.cpu().detach().numpy())\n",
    "            \n",
    "            # Step 4: Use the current hidden and context vectors to make a prediction to the next word\n",
    "            prediction_vector = torch.cat((context_vectors, h_t), dim=1)\n",
    "            score_for_y_t_index = self.classifier(F.dropout(prediction_vector, 0.3))\n",
    "            \n",
    "            # auxillary: collect the prediction scores\n",
    "            output_vectors.append(score_for_y_t_index)\n",
    "            \n",
    "        output_vectors = torch.stack(output_vectors).permute(1, 0, 2)\n",
    "        \n",
    "        return output_vectors\n",
    "    \n",
    "    \n",
    "class NMTModel(nn.Module):\n",
    "    \"\"\" The Neural Machine Translation Model \"\"\"\n",
    "    def __init__(self, source_vocab_size, source_embedding_size, \n",
    "                 target_vocab_size, target_embedding_size, encoding_size, \n",
    "                 target_bos_index):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            source_vocab_size (int): number of unique words in source language\n",
    "            source_embedding_size (int): size of the source embedding vectors\n",
    "            target_vocab_size (int): number of unique words in target language\n",
    "            target_embedding_size (int): size of the target embedding vectors\n",
    "            encoding_size (int): the size of the encoder RNN.  \n",
    "        \"\"\"\n",
    "        super(NMTModel, self).__init__()\n",
    "        self.encoder = NMTEncoder(num_embeddings=source_vocab_size, \n",
    "                                  embedding_size=source_embedding_size,\n",
    "                                  rnn_hidden_size=encoding_size)\n",
    "        decoding_size = encoding_size * 2\n",
    "        self.decoder = NMTDecoder(num_embeddings=target_vocab_size, \n",
    "                                  embedding_size=target_embedding_size, \n",
    "                                  rnn_hidden_size=decoding_size,\n",
    "                                  bos_index=target_bos_index)\n",
    "    \n",
    "    def forward(self, x_source, x_source_lengths, target_sequence):\n",
    "        \"\"\"The forward pass of the model\n",
    "        \n",
    "        Args:\n",
    "            x_source (torch.Tensor): the source text data tensor. \n",
    "                x_source.shape should be (batch, vectorizer.max_source_length)\n",
    "            x_source_lengths torch.Tensor): the length of the sequences in x_source \n",
    "            target_sequence (torch.Tensor): the target text data tensor\n",
    "        Returns:\n",
    "            decoded_states (torch.Tensor): prediction vectors at each output step\n",
    "        \"\"\"\n",
    "        encoder_state, final_hidden_states = self.encoder(x_source, x_source_lengths)\n",
    "        decoded_states = self.decoder(encoder_state=encoder_state, \n",
    "                                      initial_hidden_state=final_hidden_states, \n",
    "                                      target_sequence=target_sequence)\n",
    "        return decoded_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Routine and Bookkeeping Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed_everywhere(seed, cuda):\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    if cuda:\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "def handle_dirs(dirpath):\n",
    "    if not os.path.exists(dirpath):\n",
    "        os.makedirs(dirpath)\n",
    "\n",
    "def make_train_state(args):\n",
    "    return {'stop_early': False,\n",
    "            'early_stopping_step': 0,\n",
    "            'early_stopping_best_val': 1e8,\n",
    "            'learning_rate': args.learning_rate,\n",
    "            'epoch_index': 0,\n",
    "            'train_loss': [],\n",
    "            'train_acc': [],\n",
    "            'val_loss': [],\n",
    "            'val_acc': [],\n",
    "            'test_loss': -1,\n",
    "            'test_acc': -1,\n",
    "            'model_filename': args.model_state_file}\n",
    "\n",
    "def update_train_state(args, model, train_state):\n",
    "    \"\"\"Handle the training state updates.\n",
    "    Components:\n",
    "     - Early Stopping: Prevent overfitting.\n",
    "     - Model Checkpoint: Model is saved if the model is better\n",
    "    \n",
    "    :param args: main arguments\n",
    "    :param model: model to train\n",
    "    :param train_state: a dictionary representing the training state values\n",
    "    :returns:\n",
    "        a new train_state\n",
    "    \"\"\"\n",
    "\n",
    "    # Save one model at least\n",
    "    if train_state['epoch_index'] == 0:\n",
    "        torch.save(model.state_dict(), train_state['model_filename'])\n",
    "        train_state['stop_early'] = False\n",
    "\n",
    "    # Save model if performance improved\n",
    "    elif train_state['epoch_index'] >= 1:\n",
    "        loss_tm1, loss_t = train_state['val_loss'][-2:]\n",
    "         \n",
    "        # If loss worsened\n",
    "        if loss_t >= loss_tm1:\n",
    "            # Update step\n",
    "            train_state['early_stopping_step'] += 1\n",
    "        # Loss decreased\n",
    "        else:\n",
    "            # Save the best model\n",
    "            if loss_t < train_state['early_stopping_best_val']:\n",
    "                torch.save(model.state_dict(), train_state['model_filename'])\n",
    "                train_state['early_stopping_best_val'] = loss_t\n",
    "\n",
    "            # Reset early stopping step\n",
    "            train_state['early_stopping_step'] = 0\n",
    "\n",
    "        # Stop early ?\n",
    "        train_state['stop_early'] = \\\n",
    "            train_state['early_stopping_step'] >= args.early_stopping_criteria\n",
    "\n",
    "    return train_state\n",
    "\n",
    "def normalize_sizes(y_pred, y_true):\n",
    "    \"\"\"Normalize tensor sizes\n",
    "    \n",
    "    Args:\n",
    "        y_pred (torch.Tensor): the output of the model\n",
    "            If a 3-dimensional tensor, reshapes to a matrix\n",
    "        y_true (torch.Tensor): the target predictions\n",
    "            If a matrix, reshapes to be a vector\n",
    "    \"\"\"\n",
    "    if len(y_pred.size()) == 3:\n",
    "        y_pred = y_pred.contiguous().view(-1, y_pred.size(2))\n",
    "    if len(y_true.size()) == 2:\n",
    "        y_true = y_true.contiguous().view(-1)\n",
    "    return y_pred, y_true\n",
    "\n",
    "def compute_accuracy(y_pred, y_true, mask_index):\n",
    "    y_pred, y_true = normalize_sizes(y_pred, y_true)\n",
    "\n",
    "    _, y_pred_indices = y_pred.max(dim=1)\n",
    "    \n",
    "    correct_indices = torch.eq(y_pred_indices, y_true).float()\n",
    "    valid_indices = torch.ne(y_true, mask_index).float()\n",
    "    \n",
    "    n_correct = (correct_indices * valid_indices).sum().item()\n",
    "    n_valid = valid_indices.sum().item()\n",
    "\n",
    "    return n_correct / n_valid * 100\n",
    "\n",
    "def sequence_loss(y_pred, y_true, mask_index):\n",
    "    y_pred, y_true = normalize_sizes(y_pred, y_true)\n",
    "    return F.cross_entropy(y_pred, y_true, ignore_index=mask_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expanded filepaths: \n",
      "\tmodel_storage/ch8/nmt_luong_no_sampling/vectorizer.json\n",
      "\tmodel_storage/ch8/nmt_luong_no_sampling/model.pth\n",
      "Using CUDA: False\n"
     ]
    }
   ],
   "source": [
    "args = Namespace(dataset_csv=\"data/nmt/simplest_eng_fra.csv\",\n",
    "                 vectorizer_file=\"vectorizer.json\",\n",
    "                 model_state_file=\"model.pth\",\n",
    "                 save_dir=\"model_storage/ch8/nmt_luong_no_sampling\",\n",
    "                 reload_from_files=True,\n",
    "                 expand_filepaths_to_save_dir=True,\n",
    "                 cuda=False,\n",
    "                 seed=1337,\n",
    "                 learning_rate=5e-4,\n",
    "                 batch_size=64,\n",
    "                 num_epochs=100,\n",
    "                 early_stopping_criteria=5,              \n",
    "                 source_embedding_size=64, \n",
    "                 target_embedding_size=64,\n",
    "                 encoding_size=64,\n",
    "                 catch_keyboard_interrupt=True)\n",
    "\n",
    "if args.expand_filepaths_to_save_dir:\n",
    "    args.vectorizer_file = os.path.join(args.save_dir,\n",
    "                                        args.vectorizer_file)\n",
    "\n",
    "    args.model_state_file = os.path.join(args.save_dir,\n",
    "                                         args.model_state_file)\n",
    "    \n",
    "    print(\"Expanded filepaths: \")\n",
    "    print(\"\\t{}\".format(args.vectorizer_file))\n",
    "    print(\"\\t{}\".format(args.model_state_file))\n",
    "    \n",
    "# Check CUDA\n",
    "if not torch.cuda.is_available():\n",
    "    args.cuda = False\n",
    "\n",
    "args.device = torch.device(\"cuda\" if args.cuda else \"cpu\")\n",
    "    \n",
    "print(\"Using CUDA: {}\".format(args.cuda))\n",
    "\n",
    "# Set seed for reproducibility\n",
    "set_seed_everywhere(args.seed, args.cuda)\n",
    "\n",
    "# handle dirs\n",
    "handle_dirs(args.save_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if args.reload_from_files and os.path.exists(args.vectorizer_file):\n",
    "    # training from a checkpoint\n",
    "    dataset = NMTDataset.load_dataset_and_load_vectorizer(args.dataset_csv,\n",
    "                                                          args.vectorizer_file)\n",
    "else:\n",
    "    # create dataset and vectorizer\n",
    "    dataset = NMTDataset.load_dataset_and_make_vectorizer(args.dataset_csv)\n",
    "    dataset.save_vectorizer(args.vectorizer_file)\n",
    "\n",
    "vectorizer = dataset.get_vectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New model\n"
     ]
    }
   ],
   "source": [
    "model = NMTModel(source_vocab_size=len(vectorizer.source_vocab), \n",
    "                 source_embedding_size=args.source_embedding_size, \n",
    "                 target_vocab_size=len(vectorizer.target_vocab),\n",
    "                 target_embedding_size=args.target_embedding_size, \n",
    "                 encoding_size=args.encoding_size,\n",
    "                 target_bos_index=vectorizer.target_vocab.begin_seq_index)\n",
    "\n",
    "if args.reload_from_files and os.path.exists(args.model_state_file):\n",
    "    model.load_state_dict(torch.load(args.model_state_file))\n",
    "    print(\"Reloaded model\")\n",
    "else:\n",
    "    print(\"New model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4c6282bbde04870b128e96ef0da9260",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='training routine', style=ProgressStyle(description_width='ini…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd0bb05dd6c04295bd2e4702de6fb961",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='split=train', max=142, style=ProgressStyle(description_width=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bec25a08e7994b9281bc44f324067aed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='split=val', max=30, style=ProgressStyle(description_width='in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exiting loop\n"
     ]
    }
   ],
   "source": [
    "model = model.to(args.device)\n",
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,\n",
    "                                           mode='min', factor=0.5,\n",
    "                                           patience=1)\n",
    "mask_index = vectorizer.target_vocab.mask_index\n",
    "train_state = make_train_state(args)\n",
    "\n",
    "epoch_bar = tqdm_notebook(desc='training routine', \n",
    "                          total=args.num_epochs,\n",
    "                          position=0)\n",
    "\n",
    "dataset.set_split('train')\n",
    "train_bar = tqdm_notebook(desc='split=train',\n",
    "                          total=dataset.get_num_batches(args.batch_size), \n",
    "                          position=1, \n",
    "                          leave=True)\n",
    "dataset.set_split('val')\n",
    "val_bar = tqdm_notebook(desc='split=val',\n",
    "                        total=dataset.get_num_batches(args.batch_size), \n",
    "                        position=1, \n",
    "                        leave=True)\n",
    "\n",
    "try:\n",
    "    for epoch_index in range(args.num_epochs):\n",
    "        train_state['epoch_index'] = epoch_index\n",
    "\n",
    "        # Iterate over training dataset\n",
    "\n",
    "        # setup: batch generator, set loss and acc to 0, set train mode on\n",
    "        dataset.set_split('train')\n",
    "        batch_generator = generate_nmt_batches(dataset, \n",
    "                                               batch_size=args.batch_size, \n",
    "                                               device=args.device)\n",
    "        running_loss = 0.0\n",
    "        running_acc = 0.0\n",
    "        model.train()\n",
    "        \n",
    "        for batch_index, batch_dict in enumerate(batch_generator):\n",
    "            # the training routine is these 5 steps:\n",
    "\n",
    "            # --------------------------------------    \n",
    "            # step 1. zero the gradients\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # step 2. compute the output\n",
    "            y_pred = model(batch_dict['x_source'], \n",
    "                           batch_dict['x_source_length'], \n",
    "                           batch_dict['x_target'])\n",
    "\n",
    "            # step 3. compute the loss\n",
    "            loss = sequence_loss(y_pred, batch_dict['y_target'], mask_index)\n",
    "\n",
    "            # step 4. use loss to produce gradients\n",
    "            loss.backward()\n",
    "\n",
    "            # step 5. use optimizer to take gradient step\n",
    "            optimizer.step()\n",
    "            # -----------------------------------------\n",
    "            # compute the running loss and running accuracy\n",
    "            running_loss += (loss.item() - running_loss) / (batch_index + 1)\n",
    "\n",
    "            acc_t = compute_accuracy(y_pred, batch_dict['y_target'], mask_index)\n",
    "            running_acc += (acc_t - running_acc) / (batch_index + 1)\n",
    "            \n",
    "            # update bar\n",
    "            train_bar.set_postfix(loss=running_loss, acc=running_acc, \n",
    "                            epoch=epoch_index)\n",
    "            train_bar.update()\n",
    "\n",
    "        train_state['train_loss'].append(running_loss)\n",
    "        train_state['train_acc'].append(running_acc)\n",
    "\n",
    "        # Iterate over val dataset\n",
    "\n",
    "        # setup: batch generator, set loss and acc to 0; set eval mode on\n",
    "        dataset.set_split('val')\n",
    "        batch_generator = generate_nmt_batches(dataset, \n",
    "                                               batch_size=args.batch_size, \n",
    "                                               device=args.device)\n",
    "        running_loss = 0.\n",
    "        running_acc = 0.\n",
    "        model.eval()\n",
    "\n",
    "        for batch_index, batch_dict in enumerate(batch_generator):\n",
    "            # compute the output\n",
    "            y_pred = model(batch_dict['x_source'], \n",
    "                           batch_dict['x_source_length'], \n",
    "                           batch_dict['x_target'])\n",
    "\n",
    "            # step 3. compute the loss\n",
    "            loss = sequence_loss(y_pred, batch_dict['y_target'], mask_index)\n",
    "\n",
    "            # compute the running loss and accuracy\n",
    "            running_loss += (loss.item() - running_loss) / (batch_index + 1)\n",
    "            \n",
    "            acc_t = compute_accuracy(y_pred, batch_dict['y_target'], mask_index)\n",
    "            running_acc += (acc_t - running_acc) / (batch_index + 1)\n",
    "            \n",
    "            # Update bar\n",
    "            val_bar.set_postfix(loss=running_loss, acc=running_acc, \n",
    "                            epoch=epoch_index)\n",
    "            val_bar.update()\n",
    "\n",
    "        train_state['val_loss'].append(running_loss)\n",
    "        train_state['val_acc'].append(running_acc)\n",
    "\n",
    "        train_state = update_train_state(args=args, model=model, \n",
    "                                         train_state=train_state)\n",
    "\n",
    "        scheduler.step(train_state['val_loss'][-1])\n",
    "\n",
    "        if train_state['stop_early']:\n",
    "            break\n",
    "            \n",
    "        train_bar.n = 0\n",
    "        val_bar.n = 0\n",
    "        epoch_bar.set_postfix(best_val=train_state['early_stopping_best_val'] )\n",
    "        epoch_bar.update()\n",
    "        \n",
    "except KeyboardInterrupt:\n",
    "    print(\"Exiting loop\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NMTModel(\n",
       "  (encoder): NMTEncoder(\n",
       "    (source_embedding): Embedding(3025, 64, padding_idx=0)\n",
       "    (birnn): GRU(64, 64, batch_first=True, bidirectional=True)\n",
       "  )\n",
       "  (decoder): NMTDecoder(\n",
       "    (target_embedding): Embedding(4902, 64, padding_idx=0)\n",
       "    (gru_cell): GRUCell(192, 128)\n",
       "    (hidden_map): Linear(in_features=128, out_features=128, bias=True)\n",
       "    (classifier): Linear(in_features=256, out_features=4902, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.translate import bleu_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "chencherry = bleu_score.SmoothingFunction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentence_from_indices(indices, vocab, strict=True, return_string=True):\n",
    "    ignore_indices = set([vocab.mask_index, vocab.begin_seq_index, vocab.end_seq_index])\n",
    "    out = []\n",
    "    for index in indices:\n",
    "        if index == vocab.begin_seq_index and strict:\n",
    "            continue\n",
    "        elif index == vocab.end_seq_index and strict:\n",
    "            break\n",
    "        else:\n",
    "            out.append(vocab.lookup_index(index))\n",
    "    if return_string:\n",
    "        return \" \".join(out)\n",
    "    else:\n",
    "        return out\n",
    "    \n",
    "class NMTSampler:\n",
    "    def __init__(self, vectorizer, model):\n",
    "        self.vectorizer = vectorizer\n",
    "        self.model = model\n",
    "    \n",
    "    def apply_to_batch(self, batch_dict):\n",
    "        self._last_batch = batch_dict\n",
    "        y_pred = self.model(x_source=batch_dict['x_source'], \n",
    "                            x_source_lengths=batch_dict['x_source_length'], \n",
    "                            target_sequence=batch_dict['x_target'])\n",
    "        self._last_batch['y_pred'] = y_pred\n",
    "        \n",
    "        attention_batched = np.stack(self.model.decoder._cached_p_attn).transpose(1, 0, 2)\n",
    "        self._last_batch['attention'] = attention_batched\n",
    "        \n",
    "    def _get_source_sentence(self, index, return_string=True):\n",
    "        indices = self._last_batch['x_source'][index].cpu().detach().numpy()\n",
    "        vocab = self.vectorizer.source_vocab\n",
    "        return sentence_from_indices(indices, vocab, return_string=return_string)\n",
    "\n",
    "    def _get_reference_sentence(self, index, return_string=True):\n",
    "        indices = self._last_batch['y_target'][index].cpu().detach().numpy()\n",
    "        vocab = self.vectorizer.target_vocab\n",
    "        return sentence_from_indices(indices, vocab, return_string=return_string)\n",
    "    \n",
    "    def _get_sampled_sentence(self, index, return_string=True):\n",
    "        _, all_indices = torch.max(self._last_batch['y_pred'], dim=2)\n",
    "        sentence_indices = all_indices[index].cpu().detach().numpy()\n",
    "        vocab = self.vectorizer.target_vocab\n",
    "        return sentence_from_indices(sentence_indices, vocab, return_string=return_string)\n",
    "\n",
    "    def get_ith_item(self, index, return_string=True):\n",
    "        output = {\"source\": self._get_source_sentence(index, return_string=return_string), \n",
    "                  \"reference\": self._get_reference_sentence(index, return_string=return_string), \n",
    "                  \"sampled\": self._get_sampled_sentence(index, return_string=return_string),\n",
    "                  \"attention\": self._last_batch['attention'][index]}\n",
    "        \n",
    "        reference = output['reference']\n",
    "        hypothesis = output['sampled']\n",
    "        \n",
    "        if not return_string:\n",
    "            reference = \" \".join(reference)\n",
    "            hypothesis = \" \".join(hypothesis)\n",
    "        \n",
    "        output['bleu-4'] = bleu_score.sentence_bleu(references=[reference],\n",
    "                                                    hypothesis=hypothesis,\n",
    "                                                    smoothing_function=chencherry.method1)\n",
    "        \n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.eval().to(args.device)\n",
    "\n",
    "sampler = NMTSampler(vectorizer, model)\n",
    "\n",
    "dataset.set_split('test')\n",
    "batch_generator = generate_nmt_batches(dataset, \n",
    "                                       batch_size=args.batch_size, \n",
    "                                       device=args.device)\n",
    "\n",
    "test_results = []\n",
    "for batch_dict in batch_generator:\n",
    "    sampler.apply_to_batch(batch_dict)\n",
    "    for i in range(args.batch_size):\n",
    "        test_results.append(sampler.get_ith_item(i, False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.25407270926974085, 0.238016853527947)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([r['bleu-4'] for r in test_results], bins=100);\n",
    "np.mean([r['bleu-4'] for r in test_results]), np.median([r['bleu-4'] for r in test_results])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.set_split('val')\n",
    "batch_generator = generate_nmt_batches(dataset, \n",
    "                                       batch_size=args.batch_size, \n",
    "                                       device=args.device)\n",
    "batch_dict = next(batch_generator)\n",
    "\n",
    "model = model.eval().to(args.device)\n",
    "sampler = NMTSampler(vectorizer, model)\n",
    "sampler.apply_to_batch(batch_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results = []\n",
    "for i in range(args.batch_size):\n",
    "    all_results.append(sampler.get_ith_item(i, False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_results = [x for x in all_results if x['bleu-4']>0.1]\n",
    "len(top_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brian/anaconda3/envs/nlpbook/lib/python3.7/site-packages/matplotlib/pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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y3AHABvVVNyKif11W5a1VBgw8ko4GfgCcX57alGI20k5xOHCkpHuAORQDDgD+GZgk6d5y8rtjyvOfB/aWNIeiy+13dVc4IqKdVelq+zTF8qe/AbD9UJU1tYcb248C2zccf73h8v69pH8KmNzL+aeBdzehihERq6xThlMvtr2k+9mGpNWgzeYJj4gYIVrZhVZVleHUt0j6d2AtSe8CrgB+3NxqRUREp6oSeE4G/gTcRzFx6AzglGZWKiIiVk5HvMdjexlwgaTvANsBT7T7LAYRvVlSYw9ynWW91PWXgRMNkd+/WF9ZdWmD12LaTp8tHknfkrRdub8exVxtlwB3SzqspvpFRMQgtPtw6r1szyn3jwAetP0WYGfgpKbXLCIiOlJ/XW1LGva7BxVg+w95ez8iYnjqaoMHIf0FnmckvZdiFue3A0fCK8Op16qhbhERMUitHDRQ1UBLX/8XxczUx9v+Q3l+X+CaZlcsIiI6U39LXz9I72/0Xw9c38xKRUTEyumUF0hjAJJGt7oOERHtYsQFHklnSDq+4fhLkj4j6WuS7pd0n6TJ5bV9JP2kIe1/S/p4uf+opK9Iugv4UN33ERHRmy5X31plxAUe4CLgHwEkjQIOBRYCE4EdKRaA+5qkTSrk9bTtnWxf1vNCViCNiFboQpW3VqmyLMLrJX1b0rXl8baSjmx+1ZqjnKX6aUlvpZhl+m5gT2Ca7S7bfwRuAXapkN33+ylnqu1JtietO2bMENQ8IqIzVGnxXEwxmOAN5fGDwPF9pm4PFwIfp3gx9qJ+0i1l+d/Rmj2uvzi01YqIWDWd0tW2ke3LKacssr2UYtnodnYVxYi9XSiC6i+AyZJGS9oY2Bu4A3gM2FbSGpLWpxhKHhERq6DKejwvStqQcg0eSbsDzza1Vk1Wri90E/CM7S5JVwFvA+6huM+Tut9bknQ5cD/wCEW3XETEsNUOrYIqgecEYDowXtLtwMbAIU2tVZOVgwp2pxyNVs62/dlyW47tk+hlbjrb45pby4iIweuIwGP7LknvALYBBMyz/dem16xJJG0L/AS4yvZDra5PRMRIM2DgkfRp4NLumaolbSDpMNvnNr12TWB7LrBVq+sREdEMrRwmXVWVwQVH236m+8D2IuDo5lUpIiI6WZVnPKMlqXvV0XJ6mNWbW62IiOFh+LcfltfVBgtEV2nxXA98X9K+kvYFpgHXNbdaERExHEjaX9I8SfMlndxPuoMlWdKkgfKs0uI5CZgC/FN5/FOKFzAjImKYGcpRbWUP1zkUi4EuBGZKml4+K29Mty7wGeA3VfLtN/CUhV5i+3DgWytT8YiIqM8QD6feFZhvewGApMuAg4C5PdJ9AfgKvbyS0pt+u9psdwFbSMoznYiIDtM4mXG5TemRZFPg8YbjheW5xjx2Aja3XXmB0CpdbQuA2yVNp2FuMttnVi0kIiLqMZgWj+2pwNSVLat8Gf9MirkvK6syuOBhihcuRwHrNmxtS9I4SfcPIv3pkk5sZp0iIoahJ4DNG443K891WxfYHrhZ0qMUM8JMH2iAQZWZCz4/6KpGRERLdDGkw6lnAhMkbUkRcA4FPtJ90fazwEbdx5JuBk60Pau/TKvMXHATrHgntt9ZtebD1GhJFwB7UPxCD6JY+uEcivnoXqJ4efaBxg+Vv9h7gHdQ/P4+YfuOGusdEdGnoRxcYHuppGMpXqsZDVxke46kM4BZtqevTL5VnvE0djGtCRxMsU5Nu5sAHGb76HIG6oMp1uc5xvZDknYDzgV6C7Br254oaW+K9Xy2r63WERE1sj0DmNHj3Kl9pN2nSp5Vutru7HHqdkmd8Bf+I7Znl/t3AuMoWj9XSK+8q7xGH5+dBmD7VkmvlbR+47RCUIwWoXj/ibFjx5JVSCOiDu0wc0GVrraxDYejgJ2B9ZpWo/osbtjvAl5PsT7PxAqf7flftreuyFdGi4zbYovh/39CRERNqnS13UnxxSqKLrZHgCObWakWeQ54RNKHbF+hotmzg+17ekk7GbhJ0p7As+UDtoiIluuU9Xi2rKMiw8ThwHmSTgFeA1xGMZCgp79IurtM84ka6xcR0a8hHtXWFFW62l5DMU/b3uWpm4Hz23kxONuP0jAgwPbXGy7v30v603uc+p7t45tSuYiIDlelq+08ir/suxd++2h57qhmVSoiIlZOR7R4gF1s79hw/HNJvXU/jQhVhwtGRETvqkyZ0yVpfPeBpK1oj+dXEREjTtcgtlap0uL5LMUIrgUUI9u2oHjRMiKi4w3/jqv2U2VU242SJgDblKfm2V7c32ciIqI12voFUkm7AI/b/oPtxZImUkwr85ik023/ubZaRkREJe0wuKC/ZzznA0sAyjnJvgxcAjzLKqzfEBERI1t/XW2jG1o1k4Gptq8ErpQ0u5/PRUREi7R7i2e0pO7AtC/w84ZrVQYlRERErKC/ADINuEXSU8DLwC8AJL2JorstIiKGmWXtPLjA9pck3QhsAtxgv3I3o4Dj6qhcREQMTjt0tfXbZWb7172ce7B51RleJJ3Aq5OAXghcDVwL3EbDyqW2X25NDSMi2k+VmQtGJEk7U7wouxuwO3A0sAHFyqXn2N4OeIZiiHlExLDQhStvrZLA07c9gatsv2j7BeCHwF70vnLpCiRNkTRL0qznX3ihlgpHRLSDBJ7B67lyaa/dlban2p5ke1KWvY6IunTZlbdWSeDp2y+A90taW9I6wAfKcxERw1Y7dLXlfZw+2L5L0sXAHeWpC4FFratRRERnSODph+0zgTN7nO5r5dKIiJZrh/d40tUWERG1SosnIqKDtP0LpBER0V7aIfCkqy0iImqVFk9ERAfJ4IKIiIge0uKJiOggecYTERHRQ1o8EREdpJVzsFU14ls8ktaX9Klyfx9JP+kj3YWStq23dhERg7MMV95aZcQHHmB94FMDJbJ9lO25NdQnIqKjJfDAl4HxkmYDXwPGSPqBpAckXSpJAJJuljRJ0mhJF0u6X9J9kv6lpbWPiGgw1MsiSNpf0jxJ8yWd3Mv1EyTNlXSvpBslbTFQnnnGAycD29ueKGkf4EfAdsDvgduBt1Msdd1tIrCp7e2h6Kqrt7oREfWQNBo4B3gXsBCYKWl6j96fu4FJtl+S9E/AV4HJ/eWbFs+K7rC90PYyYDYrrjC6ANhK0tmS9gee6y2TrEAaEa2wzK68VbArMN/2AttLgMuAgxoT2L7J9kvl4a+BzQbKNIFnRf2uMGp7EbAjcDNwDMU6PSvICqQR0QpDvBDcpsDjDccLy3N9ORK4dqBM09UGzwPrVk0saSNgie0rJc0Dvte0mkVENJGkKcCUhlNTbU9dybz+AZgEvGOgtCM+8Nh+WtLtku4HXgb+OMBHNgX+R1J3a/HfmlrBiIhBWOZlldOWQaa/QPMEsHnD8WblueVI2g/4P8A7bC/ueb2nER94AGx/pI/zxzbs79Nwaadm1ykiYhiYCUyQtCVFwDkUWO77UtJbgfOB/W3/b5VME3giIjrIUL4YanuppGOB64HRwEW250g6A5hlezrlayjAFeXbJ7+zfWB/+SbwRER0kKGeMsf2DGBGj3OnNuzvN9g8M6otIiJqlRZPRGkNVFtZ9ZUEY0bV98/84jdvWFtZB88ZaBzQ0Pj1keNrKWeotHIOtqrS4omIiFqlxRMR0UGy9HVEREQPafFERHSQ6q+Ptk4CT0REB0lXW0RERA9p8UREdJAMp46IiOghLZ6IiA6SZzwjWFYgjYhWWIYrb62SwNMkWYE0IqJ36WqLiOggGVwwAkiaIekNra5HRES7SItnFdl+T6vrEBHRbdnwb/Ak8EREdJJ0tUVERPSQFk9ERAdphxaP3AYvG7W7cVtskV9yRFTy6GOPrdICtTuM26ry9829jy6oczHcV6TFExHRQdqhLZFnPBERUau0eCIiOkg7PONJ4ImI6CDDP+ykqy0iImqWFk9ERAdph662jm3xSLpZ0jxJs8vtBw3Xpkh6oNzukLRnw7X3Srpb0j2S5kr6ZGvuICKiM3VUi0fS6sBrbL9Ynjrc9qwead4LfBLY0/ZTknYCrpa0K/A0MBXY1fZCSWsA48rPbWB7UV33EhGxMoZ/e6dDWjyS3izpG8A8YOsBkn8O+KztpwBs3wV8B/g0sC5FMH66vLbY9rzyc5Ml3S/pXyVt3Iz7iIhYVR7E1iptG3gkrSPpCEm3ARcAc4EdbN/dkOzShq62r5XntgPu7JHdLGA7238GpgOPSZom6XBJowBsfws4AFgbuFXSDyTt3329l/plBdKIiF60c1fbk8C9wFG2H+gjzQpdbQOxfZSktwD7AScC7wI+Xl57HPiCpC9SBKGLKILWgb3kM5Wi2y5T5kREbTK4oLkOAZ4AfijpVElbVPzcXGDnHud2BuZ0H9i+z/Y3KYLOwY0Jy2dB5wL/BVwO/NvKVT8iYmRq28Bj+wbbk4G9gGeBH0n6maRxA3z0q8BXJG0IIGkiRYvmXEljJO3TkHYi8FiZ7t2S7gW+CNwEbGv7eNtziIgYJtrhGU87d7UBYPtp4CzgrLI10tVw+VJJL5f7T9nez/Z0SZsCv5Rk4HngH2w/KWld4CRJ5wMvAy9SdrNRDDh4n+3HaritiIiVMvw72rIsQi3yjCciqlrVZRG2GsT3zYJVLGtltW1XW0RErGiou9rK0bvzJM2XdHIv19eQ9P3y+m8qPO5I4ImIiN5JGg2cQzGKd1vgMEnb9kh2JLDI9puAbwJfGSjfBJ6IiA4yxC2eXYH5thfYXgJcBhzUI81BFC/hA/wA2FdSv114bT+4oB2sTJ+tpCnlu0BNl7Lao5yU1T7l1F1Wo8F830iaAkxpODW1R503BR5vOF4I7NYjm1fS2F4q6VlgQ+CpvspNi2f4mjJwkpQ1TMrqxHvq1LI68Z5Wmu2ptic1bLUEygSeiIjoyxPA5g3Hm5Xnek0jaTVgPcr5LvuSwBMREX2ZCUyQtGU5+/+hFPNZNpoOfKzcPwT4uQd4TyfPeIavOvuGU1Z7lJOy2qecustqivKZzbHA9cBo4CLbcySdAcyyPR34NvBdSfOBP1MEp37lBdKIiKhVutoiIqJWCTwREVGrBJ6IiKhVAs8wImlPSUeU+xtL2rKmcjeQtEMdZUVEJPAME5JOAz7HqwvLvQb4XhPLu1nSayWNBe4CLpB0ZhPK2VrSjZLuL493kHTKUJdTd1l9lP83Tcq3I+9rpJRV5z21iwSe4eMDFEtovwhg+/fAuk0sbz3bzwEfBC6xvRvFct9D7QKKYPpXANv3UmG4ZRuU1ZtvNynfTr2vkVJWnffUFhJ4ho8l5UtXBpC0TpPLW03SJsCHgZ80sZy1bd/R49zSDihrBbb/vklZd+p9jYiy6ryndpHAM3xcXq58ur6ko4EbgQubWN4ZFC+Fzbc9U9JWwENNKOcpSeN5NaAeAjzZhHLqLqtOnXpfMULlBdJhRNK7gHeXh9fb/lkr6zMUyoA2FdgDWAQ8AhzejCXE6yyrTp16XzFyJfC0mKTbbO8p6XmKv2gbpzRfRjEFxddsnztE5Z1k+6uSzqaXJTls//MQlXNCj1NrUbSwu59hDdlAhjrLqlOn3ldE5mprMdt7lj97HUggaUPgl8CQBB7gt+XPWUOUX1+672cbYBfgRxRB9aNAz+cV7VRWnTr1vmKES4unDUjaxHZb9ulLuhX4e9vPl8frAtfY3rudy6pTp95XjFxp8bSBZgQdSTfRe1fbO4e4qNcDSxqOl5TnmqHOsurUqfcVI1QCz8h1YsP+msDBNGeI7iXAHZKuKo/fD1zchHLqLqtOnXpfMUKlqy1eIekO27s2Id+dgL3Kw1tt3z3UZbSirDp16n3FyJTAM0KVU+V0GwVMAs6yvU2LqhQRI0S62kauO3l1+PZfgUeBI1tZoYgYGTJzwcj1OWCi7S2B71K8G/JSa6sUESNBAs/IdYrt5yTtCbyTYnqe81pcp4gYARJ4Rq6u8uffAxfYvgZYvYX1iYgRIoFn5HqinJR0MjBD0hrk/4eIqEFGtY1QktYG9gfus/1QuUTCW2zf0OKqRUSHS+CJiIhapWslIiJqlcATERG1yguk0XHKpSRuLA//hmIE35/K411tL+n1g6tW5k7A62xf18u1MRTD1begne5sAAADW0lEQVSjeGF3EfB3tgf93pSkDwJzbT+wilWOaJkEnug4tp8GJgJIOh14wfbXq35e0mjbXQOnXM5OwPbACoEH+Bfgd7YPLfP/W4rZIlbGBykWCEzgibaVrrYYUST9WNKdkuZIOqo8t5qkZyT9p6R7gV0lHShpXpn2bElXl2nHSLpY0h2S7pb0PklrAacCh0uaLemQHsVuAjzRfWD7Adt/LfP7WJnXbEnnShrVUJ8vS7pH0q8kvU7SXsB7gG+W6cdJmiDp+rKet0rausz3e5LOkvRLSQskfaDhd/Dvku4r8/5Sea7XfCKawna2bB27AacDJzYcjy1/rg3MBTagaPkb+GDDtYXAFhRdY1cAV5fXvgocWu5vADxIsazEUcB/9lGHnSm6+n4JfAF4U3l+e+BqYLXyeCrwkYb6HFCePxM4udz/HvD+hrxvAsaX+28HbmhIN62s/w7AA+X59wG/ANbq8fvoNZ9s2ZqxpastRpp/kXRgub8ZMB6YTbG4Wvd6N9sC82w/BiBpGvCP5bV3AwdIOrk8XhN4Y38F2r5T0lblZ/cDZknatdzfpTwGWAt4vPzYy7avLffv5NUlEV4haX1gd+DK8vOwfPf51bYN3Ctp0/LcfsBFtl8u6/bnCvlEDKn8zxUjhqT9gL2B3W2/LOk2isABxRd9lZfaRNHieLhH3v0uQ+1i2eorKb7cBRxQ5nWR7f/okddqLL/iaBe9/1sV8JTtiX0Uu7hH2r4MlE/EkMoznhhJ1gP+XAad7ShaG72ZC2wjafMySExuuHY9cFz3gaS3lrvPA+v2lpmkPctWBeXURG8GHgN+BnxY0kbltQ0l9dt6aizH9iLgye7nN+XzoR0H+PxPgU+Uz6WQNHYl84lYaQk8MZJcA6wtaS7wReA3vSVyMcz5WIrAMAt4Bni2vPx5YJ3y4fwcimdIAD8HdiwHHPQcXDAB+IWk+4C7gF8BP7J9X5nfz8pBDTcArx/gHqYB/949uAA4FDhG0j3AHOC9/X3Y9k8oRt7NkjSbYsQdg80nYlVkypyIXkgaY/uFssVzPsWcdme3ul4RnSAtnoje/VPZIphL8dD/ghbXJ6JjpMUTERG1SosnIiJqlcATERG1SuCJiIhaJfBEREStEngiIqJWCTwREVGr/w/Y/9jl5vzmZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YSdItwJepuggBngC2l3Qz8BaqARVQvcv2VUnzqEYG9p7/GvBxSTcA6zTkfwWweQZiRMRo0tWzvEtaDVgDWKeMhuut5QtYdtjimGJ7MbB7P5f6fUfL9lyea401nr8V2Krh1FHl/IPAa5a/phER48tg3YMfBT4NrEe16GNv0HoU+G6L6xUREW3WyRZUXQMGLdvfAr4l6VDb32ljnSIiogN6ujlo9bL9HUk7sux6Wme0sF4RERHLqLMI5JnAJsBcnltPy1Qv1EZExBjR1d2DDaYDm5ehiRERER1TJ2jdDLwMuK/FdYmIiA5a6rHR0loHWCDpWhqmb7K9V8tqFRERbdfMGTFape40ThERER1XZ/TgbyRNBqbavkzSGlTzSEVExBjS0wXdg3WmcfoIcD5wcjm1PtXMvBEREW1Vp3vwEKplk/8IYPtPkl7S0lpFRETbdcOQ9zrraS0uCxcCIGklhl6SY1ySNKVMptv3/LGSdu1EnSIixpI6La3fSPocsLqktwL/DPy8tdUaWxqn4o+IGK3GxDMt4Ejgb1TLyH+Uam2Uo1pZqS63oqRTJM2XdKmk1SWdJmlfAElflrRA0jxJX+t0ZSMiei21am+dUmf04FLgFEmnA1sA92Z2jEFNBQ6w/RFJPwb26b0g6UXAu4DNbFvS2v1lIGkGMANg0qRJTJzQ74ooERHjzoAtLUnfk7RF2V+Lau7BM4AbJB3Qpvp1ozvL+lpQLekypeHaI8DfgR9IejfwZH8Z2J5pe7rt6QlYEdEuPVbtrVMG6x7cyfb8sn8QcJvtV1MtH/+Zltesey1u2O/h+TPjL6EaiXk+sCfVCskREVHTYN2DTzfsvxU4D8D2/0qj/2HdaCRpArCG7Qsl/Ra4o9N1iojo1dMFD34GC1oPS9oTuBd4PXAwPDvkffU21G0smgj8TNJqVCtBH9bh+kREPKvbJ8z9KPBtqhneP237f8v5XYBftrpi3cj2XcCWDcf9jQ7cvm0ViogYYwYMWrZvA3br5/wlwCWtrFRERLTfWHlPKyIiYlSoMyNGRESMA90+ECMiIsaRHsZA96Ckl0r6gaSLyvHmkg5ufdUiIiKer84zrdOoBl6sV45vAz7dqgpFRERn9Lj+1il1gtY6tn8MLIVnZ3XoaWmtIiIi+lHnmdYTZaJXA0jagWoOvYiIGEO6oTVSJ2gdBswCNilTD70Y2LeltYqIiOhHnaVJrpf0JuCVVFMPLbT9TMtrFhERbdUNLa06owcPASbYnm/7ZmCCpH9ufdUiIqKdelDtrVPqDMT4iO2Hew9sPwR8pHVVGvskrZ3AHxExfHWC1opqWItE0orAKq2rUncps94P19pAglZEjCo9du2tU+oErUuAcyXtImkX4GzG2OKFkqZIulXSWZJukXS+pDUkHS3pOkk3S5rZG7wlXSnpm5JmA5+S9GJJPylpr5P0+pLuGEmnlvR3SPpkKfLLVANb5kr6aoc+dkRES0naTdJCSbdLOnKQdPtIsqTpQ+VZp5XwGWAG8PFy/Cvg+7Vq3F1eCRxs+7eSTqVqCX3X9rEAks6kWm345yX9Kranl2v/DXzD9jWSNqIK9K8q6TYD3ky1ltZCSScBRwJb2p7WX0UkzaD6njNp0iQmTpjQ/E8bEdFHMwdilF65E6gWEb4HuE7SLNsL+qSbCHwK+GOdfAcNWqXQM2wfCHxvJBXvInfb/m3Z/xHwSeBOSZ8B1gAmAfN5Lmid23DvrsDmDb2oLyirFAP80vZiYLGkvwIvHaoitmcCMwGmTJ7cBVNYRsRY0OTRg9sDt9u+A0DSOcA7gQV90n0B+ApwRJ1MB+0etN0DTJY0Hp5h9Q0OBk4E9rX9auAUYLWG60807K8A7GB7WtnWt/14uba4IV0PmaQ4IsYASTMkzW7YZvRJsj5wd8PxPeVcYx7bAhvarr2wcJ1foHcAv5U0i4Zf1LaPr1tIl9hI0uts/x54L3ANsCNwf2k17QucP8C9lwKHAl8FkDTN9txBynqMqrswImLUGE5Lq7FHaCQkrQAcD3xoOPfVGYjxZ+AXJe3Ehm2sWQgcIukW4IXASVStq5upnlFdN8i9nwSmS5onaQHwscEKsv0A1R8CN2cgRkSMUfcCGzYcb1DO9ZoIbAlcKekuYAdg1lCDMeQODl0cLSRNAX5he8sOV2UZeaYVEXXdtWjRcr31e/zWu9b+fXPYjZcNWlZ5Heg2YBeqYHUd8F7b8wdIfyVwuO3Zg+U7ZPegpCtY9nkPtt8y1L0REdE9mjkQw/YSSZ+g6qlaETjV9nxJxwKzbc8aSb51nmkd3rC/GrAPsGQkhY1Wtu+iaqZGREST2L4QuLDPuaMHSLtznTzrTJg7p8+p30q6tk7mERHRPTo500VddboHJzUcrgBsB6zVshpFREQMoE734ByqZ1qi6ha8Ezi4lZWKiIj264alSep0D27cjopEREQMpU734MpU8w6+sZy6Ejg5C0FGRIwtPcsOFB916nQPngSsTDWlEcD7y7kPt6pSERHRfmMlaL3G9tYNx7+WdGOrKhQRETGQOtM49UjapPdA0svpjud1ERExDD3D2DqlTkvrCOAKSXdQjSCcDBzU0lqNQZLusj2l0/WIiOhmdUYPXi5pKtUiiQALy/pQERExhnTDy8UDdg9Keo2klwGUIDWNarGur/Z54Tjq+RuApHUlXSVpbpnlfadOVywiAqqBGHW3ThnsmdbJwNMAkt4IfBk4A3iE5VhDZbyy/Zqy+17gEtvTgK2BwdbdioiIBoN1D65o+8Gyvx8w0/ZPgJ9Iyi/akbsOOLW8/3ZBf4tFlhVAZwBMmjSJiRMmtLmKETEedcOQ98FaWiuW9VCgWg/l1w3XsmT8CNm+iupF7XuB0yR9oJ80M21Ptz09ASsi4jmDBZ+zgd9Iuh94CrgaQNIrqLoIYwQkTQbusX2KpFWBbam6XSMiOmppFwzEGDBo2f6SpMuBdYFL/dwSxysAh7ajcmPUzsARkp4BHgeWaWlFRHRCN3QPDtrNZ/sP/Zy7rXXVGftsnw6c3ul6RER0ozybiogIoDtaWnWmcYqIiBgV0tKKiAigy2fEiIiIGG3S0oqICKA7nmklaEVEBNAd72mlezAiIrpGWloREQF0R/dgWloREdE10tKKiAigO1paCVoREQFkIEZXkTRF0s1NyOdDkr5b9veWtHnDtSslTV/eMiIixqsErdbaG9h8yFQREaNAD669dUqC1vOtKOkUSfMlXSppdUmbSLpY0hxJV0vaDEDSOyT9UdINki6T9NLGjCTtCOwFfFXSXEmblEvvkXStpNsk7dTmzxcR0dUStJ5vKnCC7S2Ah4F9gJnAoba3Aw4HTixprwF2sL0NcA7wmcaMbP8OmAUcYXua7T+XSyvZ3h74NPD5Vn+giIi6euzaW6dkIMbz3Wl7btmfA0wBdgTOk9SbZtXydQPgXEnrAqsAd9Ys46d98l+GpBnADIBJkyYxccKE+p8gImKElnbB6MG0tJ5vccN+DzAJeLi0lHq3V5Xr3wG+a/vVwEeB1YZZRg8D/NFge6bt6banJ2BFRDwnQWtwjwJ3SnoPgCpbl2trAfeW/Q8OcP9jwMTWVjEiojma3T0oaTdJCyXdLunIfq4fJmmBpHmSLpc0eag8E7SGdiBwsKQbgfnAO8v5Y6i6DecA9w9w7znAEWWwxiYDpImIGHMkrQicAOxONYr6gMZXgIobgOm2twLOB44bMl93wctk49mUyZPzDxQRtdy1aJGGTjWwvTadVvv3zazb5g5alqTXAcfYfls5/iyA7f8aIP02VI9cXj9YvmlpRUREK6wP3N1wfE85N5CDgYuGyjSjByMiAhje3IONo5yLmbZnjqRcSe8DpgNvGiptglZERACw1Etrpy0BarAgdS+wYcPxBjw3eO1ZknYF/h14k+3Ffa/3le7BiIhoheuAqZI2lrQKsD/VhAvPKs+xTgb2sv3XOpmmpRUAHLL21LaVdcLDf2pbWbF8vjapfT8XRzzYvp+Ldo1uuv3Mw9tUUnM08+Vi20skfQK4BFgRONX2fEnHArNtzwK+CkzguQkc/mJ7r8HyTdCKiIiWsH0hcGGfc0c37O863DwTtCIiAqCjcwrWlaAVERFA5h6MiIhoqrS0IiICgKVd0D2YllZERHSNtLQiIgKA+q8Wd06CVkREAOkejIiIaKq0tCIiAsiQ9xghSTMkzZY0+7HHH+90dSIiRo0ErVHI9kzb021PnzhhQqerExHjxFK79tYp6R6MiAgg3YMxBEkXSlqv0/WIiOgWaWl1kO09Ol2HiIheaWlFREQ0UVpaEREBwNLR39BKSysiIrpHWloREQF0xzMtuQvmmhrPpkye3JZ/oF9s8bJ2FAPAnvP/t21lRYwndy1apOW5f6spL6/9+2beXXcsV1kjle7BiIjoGukejIgIALqh4y0trYiI6BppaUVEBNAdAzEStCIiAqALQla6ByMiooskaPVD0pWSFkqaW7bzG67NkHRr2a6V9IaGa3tKukHSjZIWSPpoZz5BRMTwLcW1t05J92AhaRVgZdtPlFMH2p7dJ82ewEeBN9i+X9K2wAWStgceAGYC29u+R9KqwJRy3wttP9SuzxIRMVaN+5aWpFdJ+jqwENh0iOT/Bhxh+34A29cDpwOHABOp/gh4oFxbbHthuW8/STdL+ldJL27F54iIWF4extYp4zJoSVpT0kGSrgFOARYAW9m+oSHZWQ3dg18t57YA5vTJbjawhe0HgVnAIklnSzpQ0goAtr8H7A6sAVwl6XxJu/Ve76d+MyTNljT7sccfb9rnjogYTDcErfHaPXgfMA/4sO1bB0izTPfgUGx/WNKrgV2Bw4G3Ah8q1+4GviDpi1QB7FSqgLdXP/nMpOpqbNs0ThER3WBctrSAfYF7gZ9KOlrS5Jr3LQC263NuO2B+74Htm2x/gypg7dOYsDz7OhH4NvBj4LMjq35ERPN1w0CMcRm0bF9qez9gJ+AR4GeSLpM0ZYhbjwO+IulFAJKmUbWkTpQ0QdLODWmnAYtKun+QNA/4InAFsLntT9ueT0RE1DZeuwcBsP0A8C3gW6UV1NNw+SxJT5X9+23vanuWpPWB30ky8BjwPtv3SZoIfEbSycBTwBOUrkGqwRnvsL2oDR8rImJEuuFZxLgOWo1sX9uwv/Mg6U4CTurn/GPAHgPc03fwRkREjECCVkREAN3R0hqXz7QiImJZzR7yXl7tWSjpdklH9nN9VUnnlut/rDGuIEErIiKaT9KKwAlUr/hsDhwgafM+yQ4GHrL9CuAbwFeGyjdBKyIigKa3tLYHbrd9h+2ngXOAd/ZJ806qWYUAzgd2kaTBMs0zrVHurkWLBv0H7I+kGeUF5ZYbSVl3tbGskWpXWWPxM6Ws7imnr+H8vpE0A5jRcGpmnzqvD9zdcHwP8No+2TybxvYSSY8ALwLuH6jctLTGphlDJ0lZo6SssfiZUlb3lDNitmfant6wtSXIJmhFREQr3Ats2HC8QTnXbxpJKwFrUSYdH0iCVkREtMJ1wFRJG5eln/anmlS80Szgg2V/X+DXtgd9ZJZnWmNTO/vCU1Z3lJOyuqustj/ParbyjOoTwCXAisCptudLOhaYbXsW8APgTEm3Aw9SBbZBaYigFhERMWqkezAiIrpGglZERHSNBK2IiOgaCVoxbJJWrXOuBeWuIOkFrS4nIkavBK0uJ+kmSfP62W4qC0+2wu9rnltukv5b0gskrQncDCyQdEQLytlU0uWSbi7HW0k6qtnlDFGHl42lctpRlqSXSNqod2tlWX3KHXP/Vt0iQav77Qm8o5+t93zTSHqZpO2A1SVtI2nbsu0MrNHMshpsbvtRYG/gImBj4P0tKOcU4LPAMwC251Fj+G2T/WCMldOysiTtJelPwJ3Ab6hmB7uoFWUNYCz+W3WFvKfV5dq8GvLbqFZj3gD4OtA7T9ljwOdaVObKklamClrftf1MWTW62dawfW2fuTqXtKCcAdl++1gqp8VlfQHYAbjM9jaS3gy8r0VlLWMs/lt1iwStLifpGttvkPQYz598WYBtN+0ZkO3TgdMl7WP7J83KdwgnU/0VfSNwlaTJwKMtKOd+SZtQvoeS9gXua0E50RzP2H6gPOdcwfYVkr7Z6UqrMqD3AAAF1UlEQVRF6+Xl4hg2SZ8CfkjVwjoF2BY40valbSp/JdtNbQVJejnVLAQ7Ag9RdTsd2OaWbNQk6TKq1vd/AesAfwVeY3vHjlYsWi5BK4ZN0o22t5b0NuBjwFHAmba3bVF5bwe2AFbrPWf72CblfVifU6tTPet9opRzfDPKieYqA3P+TtWjcCDVRKtn2R50stXofukejJHoffDzduCMMp/YsNf9qlWQ9D2qQR5vBr5PNanmtU0sYmL5+krgNcDPqD7f+5tcTjSR7ScaDk8fMGGMOWlpxbBJ+iGwHvByYGuqyTCvtL1dC8qaZ3urhq8TgIts79Tkcq4C3m77sXI8Efil7Tc2s5xYPv08u332Ek1+hhujU1paMRIHU3UJLrD9ZHk/5tMtKuup8vVJSetRrbWzbgvKeSnwdMPx0+VcjCK2Jw6dKsayBK0YiROApcBbgH+hGpBxPFX3WrP9QtLawHHAnHLu+y0o5wzgWkn/U473Bk5rQTkRsRzSPRjDJul629tKusH2NuXcjba3bkFZqwMfB3ai6ha6GjjJ9t9bUNa2pRyAq2zf0OwyImL5pKUVI/GMpBV57p2mF1O1vFrhdKqW3LfL8XupWkX/2OyCbF8PXN/sfCOieRK0YiS+DfwP8BJJX6Ia0deqefq2tL15w/EVkha0qKyIGOUStGLYbJ8laQ6wC9Worb1t39Ki4q6XtIPtPwBIei0wu0VlRcQol2daMapJuoXqHaq/lFMbAQup5gW07a06VbeIaL8ErRjVylyDA8o0SxHjS4JWRER0jaynFRERXSNBKyIiukZGD0Y0kPQi4PJy+DKgB/hbOd7e9tP93rh8ZW4LvMT2xf1cm0A1A8gWVCM1HwLeZvvJEZTzbqqpt25dzipHdEyCVkSDsrTFNABJxwCP2/5a3fslrWi7Z5jFbgtsCSwTtKimyfqL7f1L/psBzwwz/17vpnoJPEErula6ByNqkvRzSXMkzZf04XJuJUkPS/qmpHnA9pL2krSwpP2OpAtK2gmSTpN0raQbJL2jTFN1NHCgpLllxeRG6wL39h7YvtX2MyW/D5a85ko6sazi21ufL0u6UdLvJb1E0k7AHsA3SvopkqZKuqTU8ypJm5Z8fyTpW5J+J+kOSe9q+B58TtJNJe8vlXP95hPRErazZcvWzwYcAxzecDypfF0DWAC8kKq3wsC7G67dA0ym6s47D7igXDsO2L/svxC4jWphyw8D3xygDttRdU/+DvgC8IpyfkvgAmClcjyTaoqr3vrsXs4fT7WqNMCPqF4E7837CmCTsv964NKGdGeX+m8F3FrOv4Nq7sfV+3w/+s0nW7ZWbOkejKjvXyTtVfY3ADYB5lItY9I7O/zmwEKX98cknQ18oFz7B2B3SUeW49WoXpYekO05kl5e7t0VmC1p+7L/mnIM1YrLd5fbnrJ9Udmfw3OTAD+rzJy/A/CThvU7G38fXGDbwDxJ65dzuwKn2n6q1O3BGvlENFV+uCJqkLQr8EZgB9tPSbqGKuhAFSTqvPDYO+XVn/vkPehCk64WpvwJVWAQsHvJ61Tb/6dPXivx/HXBeuj//7mA+21PG6DYxX3SDmSofCKaKs+0IupZC3iwBKwtGHjtsAXAKyVtWALMfg3XLgEO7T2QtE3ZfQzod3FDSW8orRkkrQq8ClgEXAb8o6R1yrUXlcU4B/NsObYfAu7rfV5VnocNtbTMr4B/Ks/hkDRphPlEjFiCVkQ9vwTWKDPMfxH4Y3+JXA1F/wRVUJkNPAw8Ui7/B7BmGcgwn+qZGcCvga3L4Iy+AzGmAldLuolq2ZTfAz+zfVPJ77IyAORShl5p+Wzgc70DMYD9gY9JuhGYD+w52M22f0E1wnG2pLlUIxsZbj4RyyPTOEU0maQJth8vLa2TgZtsf6fT9YoYC9LSimi+j5eWyAKqARKndLg+EWNGWloREdE10tKKiIiukaAVERFdI0ErIiK6RoJWRER0jQStiIjoGglaERHRNf4/D85Ry5jkMnwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DqBERESMuLaCIiA7URfO74Kosx/AqSd+VdGl5PF3SYaMfWkREdLIqXXBnUQw6eE15fB/wqdEKqApJ60r6eLm/m6Sf1hDDsrF+z4iIqtr9QdRuG9i+AIpJk2wvB7pGNaqBrQt8fDAvkJRVXCMiGqRKAnpK0vqUcx1K2gl4clSjGtgXgamS5gFfASZJulDSPZLOVbl4kaSHJH1J0m3A+yTNkHSjpDsl/UjSK8rrrpE0s9zfQNJD5f4aki6QtKC8/qbu68rzX5B0R1nnq8b4dxAR0aeuQWx1qZKA/h6YQ/GFfz3FhKRHjWpUAzsWuN/2DOAYYDuKbsHpwGYUMzd0e8z29rbPp4j9H21vQ7G8xPEDvM/HgSW2pwOfpZgHr9uawI22twWuBT7aWwWSZkmaK2nu0mXptYuIsdERCcj2bcBfAm+imB1hK9t3jnZgg3Sz7cXl0hHzgCkt574PL8zova7tX5blZwNvGaDeNwPnA9i+G2j93M8B3feebu3xni+wPdv2TNsz15o0qfIHiojodFVGwR0JTLI9v/wSntQ9AKBBnm3Z7+Klw8ufqvD65bz4u1it4ns+37IuUs/3jIioVReqvNWlShfcR20/0X1gewl9dDeNoaXAWoN5ge0ngSWSdi2LPgR0t4Ye4sXutf1bXnY98H4ohp8DWw8x3oiI6KHKX+0TJan7r/1yNNkqoxtW/2w/Jul6SXcDzwD/U/GlHwFOk7QG8ADFOkcAXwUukDSLl060+m3gbEkLgHuA+dQ/ACMiYkBdbbBwtQZaXVvSV4HXAqeXRUcAD9v+9CjHVrsy2a5s+8+SpgK/ALa0/dwAL+3VlMmTm/8vIiIa4aFFi4bVN3bqtm+v/H3zsTuuGPC9JO0FnAxMBM6w/cU+rnsvcCHwBttz+6uzSgvoH4BZwMfK4yuAMyq8rhOsAVwtaWVAwMeHmnwiItpV+cf4KRSLky4GbpE0x/aCHtetBXwSuKlKvf0moPJNz7F9MHDaUAJvZ7aXAjMHvDAiomFGeHj1jsBC2w8ASDof2A9Y0OO6E4EvUTweM6B+ByHY7gImS6r1nk9ERIye1ucVy21Wj0s2Ah5uOV5clrXWsT2wie3KC5ZW6YJ7ALhe0hxahjTbPqnqm0RExNgaTAvI9mxg9lDfS9IE4CTgkMG8rkoCur/cJjDIoc8REVGPLkZ0zNMjwCYtxxuXZd3WAl4PXFPOhPZqYI6kffsbiDBgArL9z0MKNyIiOsUtwDRJm1IkngOBD3SfLJ+z3KD7WNI1wNHDHgUn6Wp4eSq1/baqkUdExNgayUEItpdL+gTF0jwTgTNtz5d0AjDX9pyh1FulC+7olv3VgPdSTF0TERHjhO1LgEt6lH2uj2t3q1JnlS64W3sUXS/p5iqVR0REPdphJoQqXXDrtRxOoJgzbZ1RiygiIsaFKl1wt1LcAxJF19uDwGGjGVRERAxP3ctWV1GlC27TsQgkIiJGzggPwx4VVbrgVqaYB6578bZrgNNtPz+KcUVERIer0gV3KrAyxdIEUKyjcypw+GgFFRERw9MRLSCKKbW3bTm+StIdoxVQRESMD1VWRO0q18IBQNJmtMf9rYiIcatrEFtdqrSAjqFYE+cBipFwk3lxJdGIiGigjngOyPaVkqYBW5ZF99p+dnTDioiITtdnF5ykN0h6NUCZcGZQLDb0lR4Pp45bktaV9PG644iI6KkLV97q0t89oNOB5wAkvQX4InAO8CTDWDeiw6wLJAFFRAxBf11wE20/Xu4fAMy2fRFwkaR5ox9aPcrZXR+3/fXy+AvAH4BVgPcDqwI/sn08RVKeWv4+rrBdaRnaiIjR1g7DsPtrAU2U1J2gdgeuajlXZfBCuzoT+DC8sMrfgcB/A9Mo1kWfAexQtgqPBe63PaOv5NO61O3SZcvG5ANERKywK2916S+RnAf8UtKjwDPAdQCSNqfohutIth+S9Jik7YBXAbcDbwD2LPcBJlEkpN9VqO+FpW6nTJ7c/D9JIiLGSJ8JyPYXJF0JbAhcbr+QJicAR41FcDU6g2Jt81dTtIh2B/6/7dNbL5I0ZawDi4iooh264PrtSrN9Yy9l941eOI3xI+AEiimIPkAxC/iJks61vUzSRsDzwFKKtdAjImKQOvlezpDZfq5civwJ213A5ZJeB9wgCWAZ8EHb90u6XtLdwKUZhBARTdH2LaDxqhx8sBPwvu4y2ycDJ/e81vYHxjC0iIiOUWUuuHFF0nRgIXCl7d/WHU9ExFB02ZW3uqQF1IPtBcBmdccRETEc7dAFlxZQRETUIi2giIgOVOcDplWlBRQREbVICygiogO1wz2gJKCIiA6UBBQv8cAPT6w7hF5t9p7P1h1CjICJdQfQj/M23bjuEHr1qd89WncI41oSUEREB8oghIiIiD6kBRQR0YFyDygiImpR5xQ7VaULLiIiapEWUEREB1rRBl1waQFFREQtkoAiIjrQSC/HIGkvSfdKWijp2F7O/72kBZLulHSlpMkD1ZkEFBER/ZI0ETgF2BuYDhxUrp3W6nZgpu1tgAuBLw9UbxJQREQHWmFX3irYEVho+wHbzwHnA/u1XmD7attPl4c3AgNOf5EEFBHRgbpw5U3SLElzW7ZZParbCHi45XhxWdaXw4BLB4oxo+AiIsY527OB2SNRl6QPAjOBvxzo2iSgiIgOtMIrRrK6R4BNWo43LsteQtIewP8D/tL2swNVmi64UdbatJ39w6vrDiciYihuAaZJ2lTSKsCBwJzWCyRtB5wO7Gv7D1UqTQtolLU2bVfcek7znwyLiI4wkg+i2l4u6RPAZRQrf5xpe76kE4C5tucAXwEmAT+QBPA72/v2V28SUEREBxrpueBsXwJc0qPscy37ewy2znTBRURELZKARoikSyS9pu44IiKg6IKrutUlXXAjxPY+dccQEdFOkoAiIjpQOyzJnQQUEdGBRvQpoFGSe0AREVGLtIAiIjpQO3TBpQUUERG1SAsoIqIDZUnuiIiIPqQFNIamveezdYcQHayr7gD68f4HF9cdwrjTDveAkoAiIjpQuuAiIiL6kBZQREQHSgsoIiKiD2kBRUR0oBXNbwAlAUVEdKJ0wUVERPQhLaCIiA6UFlBEREQf0gKKiOhAbTARQue3gCRdI+leSfPK7cKWc7Mk3VNuN0t6c8u5d0i6XdIdkhZIOqKeTxARMXgrcOWtLh3ZApK0CrCy7afKooNtz+1xzTuAI4A3235U0vbAxZJ2BB4DZgM72l4saVVgSvm6V9heMlafJSKiU3VUC0jS6yT9G3AvsMUAl/8jcIztRwFs3wacDRwJrEWRnB8rzz1r+97ydQdIulvSpyX9n9H4HBERw+VBbHVp+wQkaU1Jh0r6FfAdYAGwje3bWy47t6UL7itl2VbArT2qmwtsZftxYA6wSNJ5kg6WNAHA9mnA3sAawLWSLpS0V/f5XuKbJWmupLl/WrZsxD53RES764QuuN8DdwKH276nj2te1gU3ENuHS9oa2AM4Gng7cEh57mHgREn/QpGMzqRIXvv2Us9siu48pk6e3Aa3BSOiE2QY9tjYH3gE+KGkz0maXPF1C4AdepTtAMzvPrB9l+2vUSSf97ZeWN4r+jbwDeAC4DNDCz8iYnxq+wRk+3LbBwC7Ak8CP5b0C0lTBnjpl4EvSVofQNIMihbOtyVNkrRby7UzgEXldXtKuhP4F+BqYLrtT9meT0REQ7TDPaBO6IIDwPZjwMnAyWXrpHWByHMlPVPuP2p7D9tzJG0E/FqSgaXAB23/XtJawD9IOh14BniKsvuNYmDCO20vGoOPFRExJM3vgAO5HZ5W6hBNvQfU5KWcI8arhxYt0nBev8XkKZW/b+5b9NCw3muoOqYFFBERL8oghIiIiD6kBRQR0YGa3/5JAoqI6EjtkIDSBRcREbVIAoqI6EAj/RxQOeXYvZIWSjq2l/OrSvp+ef6mCs9iJgFFRET/JE0ETqGYemw6cJCk6T0uOwxYYntz4GvAlwaqNwkoIqIDjXALaEdgoe0HbD8HnA/s1+Oa/ShWFAC4ENhdUr/PF2UQwhi6f5gPlrWSNKuc6LRRmhoXNDe2xDU4TY0LmhXbYB5klTQLmNVSNLvH59gIeLjleDHwxh7VvHCN7eWSngTWBx7t633TAmpfswa+pBZNjQuaG1viGpymxgXNjq1PtmfbntmyjUkSTQKKiIiBPAJs0nK8cVnW6zWSVgLWoVzUsy9JQBERMZBbgGmSNpW0CnAgxaKdreYAHyn39weu8gCTjeYeUPtqRD9zL5oaFzQ3tsQ1OE2NC5od25CV93Q+AVwGTATOtD1f0gnAXNtzgO8C35O0EHicIkn1K7NhR0RELdIFFxERtUgCioiIWiQBRURELZKAImomaYKkteuOI2KsJQG1EUlTJa1a7u8m6W8lrduAuNaUNKHc30LSvpJWTlx9k/SfktaWtCZwN7BA0jF1x9UbSa+uO4beNDUuaHZsTZIE1F4uArokbU4x3HMT4D/rDQmAa4HVJG0EXA58CDir1ogKTY0LYLrtPwHvAi4FNqWIr4m+W3cAfWhqXNDs2BojCai9rLC9HHg38E3bxwAb1hwTFMP5nwbeA3zb9vuArWqOCZobF8DKZWvsXcAc28/T0DXEbP9V3TH0pqlxQbNja5IkoPbyvKSDKJ42/mlZ1oQuJUnaGTgY+FlZNrHGeLo1NS6A04GHgDWBayVNBv5Ua0QRYywJqL0cCuwMfMH2g5I2Bb5Xc0wAnwI+A/yofDp6M+DqmmOC5saF7W/Y3sj2Pi4sAt5ad1wRYykzIcSIkbRG2eXVKA2O668ougRX6y6zfUJ9EUWMrbSA2oikByU90HNrQFw7S1oA3FMebyvp2zWH1di4ACSdBhwAHAUIeB8wudagIsZYWkBtRNL6LYerUXxprWf7czWFBICkmyhmv51je7uy7G7br09cvZN0p+1tWn5OAi61vWvdsUWMlbSA2ojtx1q2R2x/HWjEaBvbD/co6qolkB6aGhfwTPnzaUmvAZ6nGSMaI8ZMlmNoI5K2bzmcAMykGf8NH5b0JsDl0OJPAr+pOSZoblwAPy0fIv4ycGtZdkaN8USMuXTBtRFJrSO4llMM4/2q7XvriaggaQPgZGAPivsZlwOftN3vaojjNS4ASasDHwN2pXj+5zrgVNt/rjWwiDGUBBRRA0kXAEuB/yiLPgCsY/v99UUVMbaSgNqIpHWA44G3lEW/BE6w/WR9UUH5PNJRwBRaugRt71tXTNDcuAAkLbA9faCyiE7WhPsHUd2ZFBNXdv+V/CHg3ymmmqnTxRRzX/0EWFFzLK2aGhfAbZJ2sn0jgKQ3AnNrjiliTKUF1EYkzbM9Y6CysSbpJttvrDOG3jQ1LgBJvwG2BH5XFr0WuJfi3p5tb1NXbBFjJS2g9vKMpDfb/hWApF14cThvnU6WdDzFTf5nuwtt31ZfSEBz4wLYq+4AIuqWBNRePgacXd4LAlhCMTFp3bam6A58Gy92dbk8rlNT46Kc+y1iXEsXXBspF6PbH5gKrAs8SdFdU+v8YZIWUqxv81ydcfTU1LgiopCZENrLj4F3An8GHgGWAU/VGlHhboqE2DRNjSsiSBdcu9nYdhPvHawL3CPpFl56r6Xu4c5NjSsiSAJqN7+WtLXtu+oOpIfj6w6gD02NKyLIPaC2Ui4tsDnwIMVf9KJBQ3Ylrc1LH/h8vMZwXtDUuCLGu7SA2svedQfQG0mzgBMo7k2toEyMwGaJKyL6khZQDJuk3wI723607lhaNTWuiChkFFyMhPuBxi15TXPjigjSAooRIGk7ijnpbuKlo83+tragaG5cEVHIPaAYCacDVwF30axJP5saV0SQFlCMAEm3296u7jh6ampcEVFIAophk/SvFKuz/oSXdnXVOty5qXFFRCEJKIZN0oO9FNt23cOwGxlXRBSSgCIiohYZhBDDJmlliqUiupcKvwY43fbztQVFc+OKiEJaQDFsks4AVgbOLos+BHTZPry+qJobV0QUkoBi2CTdYXvbgcrGWlPjiohCZkKIkdAlaWr3gaTNgK4a4+nW1LgigtwDipFxNHC1pAfK4ynAofWF84KmxhURJAHFyFgfeD3FF/y7gJ0plguvW1PjigjSBRcj47PETwGHAAAD40lEQVS2/wSsDbwV+BZwar0hAc2NKyJIAoqR0X1f5a+A79j+GbBKjfF0a2pcEUESUIyMRySdDhwAXCJpVZrxb6upcUUEGYYdI0DSGsBewF22fytpQ2Br25cnrojoSxJQRETUIt0RERFRiySgiIioRZ4Dio4laX3gyvLw1RSj4v5YHu9o+7lReM/tgVfa/nkv5yYBZwBbAQKWAP/X9tNDeJ/3AAts3zPMkCNqkwQUHcv2Y8AMAEmfB5bZ/mrV10uaaHuwU/dsT/Hw68sSEPB3wO9sH1jW/xfAUGfmfg/FMuNJQNG20gUX45Kkn0i6VdJ8SYeXZStJekLS1yXdCewoaV9J95bXflPSxeW1kySdJelmSbdLeqek1YHPAQdLmidp/x5vuyHwSPeB7Xu6l4aQ9JGyrnmSvi1pQks8X5R0h6QbJL1S0q7APsDXyuunSJom6bIyzmslbVHW+x+STpb0a0kPSHp3y+/gnyTdVdb9hbKs13oiRoXtbNk6fgM+Dxzdcrxe+XMNYAHwCooeAQPvaTm3GJhM0WX2A+Di8tyXgQPL/VcA9wGrAYcDX+8jhh0ougB/DZwIbF6Wvx64GFipPJ4NfKAlnr3L8pOAY8v9/wDe1VL31cDUcn8X4PKW684r498GuKcsfydwHbB6j99Hr/VkyzYaW7rgYrz6O0n7lvsbA1OBecBzwI/K8unAvbYXAUg6D/hweW5PYG9Jx5bHqwGv7e8Nbd9azsi9J7AHMFfSjuX+G8pjgNWBh8uXPWP70nL/VmDXnvVKWhfYCbiofD28tHv9YtsG7pS0UVm2B3Cm7WfK2B6vUE/EiMo/rhh3JO1BsUrqTrafkfQrigQCxRd+lYfjRNECub9H3W/p43oAbC8FLqL4khewd1nXmbY/26OulSgSYrcuev9/VsCjtmf08bbP9ri2LwPVEzGicg8oxqN1gMfL5LMVReujNwuALSVtUiaLA1rOXQYc1X0gabtydymwVm+VSXpz2cqgnBbodcAi4BfA+yVtUJ5bX1K/ranW97G9BPh99/2d8v7RQIvuXQH8dXnfCknrDbGeiCFLAorx6GfAGpIWAP8C3NTbRS6GR3+CIkHMBZ7gxeUc/hlYs7yJP5/iHhPAVcC25cCEnoMQpgHXSboLuA24Afix7bvK+n5RDn64HHjVAJ/hPOCfugchAAcCfyPpDmA+8I7+Xmz7pxQj9eZKmkcxQo/B1hMxHJmKJ6IfkibZXla2gE6nmFfum3XHFdEJ0gKK6N/HyhbCAorBAd+pOZ6IjpEWUERE1CItoIiIqEUSUERE1CIJKCIiapEEFBERtUgCioiIWvwvmG/tY2ZVW2IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2xBUR0XRDLX39O+DAAc5fRTHEeNKRtD4wD7jV9jXdjicior+mvMcTJduPANt3O46IiDpL4omIaJA6DC6oMldbRETEuKmyLMLzJZ0j6cryeEdJx7U/tIiIGKkeVHnrliotnvMoBhO8sDz+HXB8uwKKiIjR63H1rVuqJJ6NbV8M9ALYXsXg77dEREQMqcrggsclbUS5Bo+kvYFH2xpVRESMSh1aBVUSz4eBucC2kn4FbEIxz1lERMSIDZt4bN8s6TXAiykm01xse8B5zCIiorvq0OKpMqrt/cA02wtt3wFMk/S+9ocWEREj1ZRRbe8p39gHoJwY8z3tCykiIpqsyjOeqZLUt+qopKnAGu0NKyIiRqOnBgtEV2nxXAVcJGk/SfsBFwI/am9YERExEUg6UNJiSUsknThEubdIsqQ9hquzSovno8Bs4O/K4x9TrMIZERETzHgOLih7uM6gWAx0GXCjpLm2F/UrNx34EHBDlXqHTDzll55v+yjgG6MJPCIiOmecR7XtBSyxfReApO8AhwKL+pX7NPBF4IQqlQ7Z1Wa7B5ghKc90IiIaRtJsSfNbttn9imwO3NtyvKw811rHbsCWtisvEFqlq+0u4FeS5gKP9520fVrVL4mIiM4YSYvH9hxgzmi/S9IU4DTgmJF8rkri+UO5TQGmjziyiIioq/uALVuOtyjP9ZkOvBS4ThLAC4C5kg6xPX+wSqvMXPCpUYUbEREd18O4Dqe+EZgpaWuKhHME8Dd9F20/CmzcdyzpOuAjQyUdqJB4JF0Lz70T26+tGnlERNSP7VWSPkDxWs1U4FzbCyWdAsy3PXc09VbpavtIy/5awFuAVaP5soiIaK/xnqvN9hXAFf3OfWKQsvtWqbNKV9tN/U79StK8KpVPFJJOBlbY/uduxxIR0U51mLmgSlfbhi2HU4DdgfXaFlENSVqtXCAvIiKGUaWr7SaKZzyi6GK7GziunUGNlaR3UHQRGriNYlRe37VtKd7E3QT4C8WEp/eX5ba23StpXeBOYBvgRf3L275T0nnA/wC7Ar+iWLcoIqKr6rAsQpWutq07Ech4kbQTcBLwCtsPli22/9NSZA7wt7Z/L+nlwJm2XyvpFuA1wLXAwcBVtp+S9JzyQN/Aii3K76nD33VExIRQpattdYp52l5dnroOOGsCLwb3WuAS2w8C2H64HF+OpGnAK4BL+s4Ba5Y/LwIOp0g8RwBnDlOe8nsGTDrlG8CzATbccEOmT5s2LjcXETGUcR5O3RZVutq+DqxO8X/6AG8vz727XUG10RTgEduzBrg2F/hc2ULaHfgpsO4Q5aFlJof+Wt8I3mrGjIn/LyEiGqEOiafKsgh72n6n7Z+W27HAnu0ObAx+CrxV0kbw7MERtv8M3C3preU1SdqlvLaC4mWp04Ef2O4ZqnxERIxOlcTTUz6QB0DSNkzg51e2FwKfBX4m6VaKeYRaHQUcV15bSDHTap+LgKPLn1XKR0RMKD0j2LqlSlfbCcC1ku6iGNk2Azi2rVGNke1vAd8a5NrdwIGDXLsUnr0Q+WDlbR8z5kAjIiahKqParpE0E3hxeWqx7SfbG1ZERIxGrV8glbQncK/tP9p+UtIsiulylko62fbDHYsyIiIqqfvggrOAlQCSXg18ATgfeJQxrN8QERGT21BdbVNbWjWHA3Nsfxf4bvmyZURETDB1b/FMldSXmPajGKbcp8qghIiIiOcYKoFcSDEk+UHgCeAXAJK2o+hui4iICaa3zoMLbH9W0jXAZsDV9tN3MwX4YCeCi4iI5hmyy8z29QOc+137womIiLGowzOePKuJiGiQOiSeKlPmREREjJu0eDrg7u9/qdshjLtvvP3sbocw7r74SDN7kZv6H/nvL/t8t0OYkOowc0FaPBER0VFN/Z+hiIhJqQ7PeJJ4IiIapA7v8aSrLSIiOiotnoiIBqlDV1taPBER0VFp8URENEgdWjxJPBERDZLBBREREf2kxRMR0SB16GpLiyciIjoqLZ6IiAbJXG0RERH9pMUTEdEgvXnGExERndRjV96qkHSgpMWSlkg6cYDrH5a0SNJtkq6RNGO4OpN42kTSbEnzJc2fc+lPuh1ORMSISZoKnAG8HtgROFLSjv2KLQD2sL0zcClw6nD1pqutTWzPAeYA+LaLJ37bNyIaYZxfIN0LWGL7LgBJ3wEOBRb1FbB9bUv564Gjh6s0LZ6IiBjM5sC9LcfLynODOQ64crhKk3jGSNIVkl7Y7TgiIqB4gbTq1vpIoNxmj/Z7JR0N7AF8abiy6WobI9sHdTuGiIg+ve6tXLb1kcAg7gO2bDneojz3LJL2B/4ReI3tJ4f73rR4IiJiMDcCMyVtLWkN4AhgbmsBSbsCZwGH2H6gSqVp8URENMh4vsdje5WkDwBXAVOBc20vlHQKMN/2XIqutWnAJZIA/sv2IUPVm8QTERGDsn0FcEW/c59o2d9/pHUm8URENEgd5mpL4omIaJBMmRMREdFPWjwREQ2Spa8jIiL6SYsnIqJBqr8+2j1p8UREREfJNegPrLutZ8xo3B/y3B0263YI4+4Nv72/2yFEcM/SpRrL5/fZ5iWVf9/88q47x/Rdo5WutoiIBslw6oiIiH7S4omIaJAMp46IiOgnLZ6IiAapwzOeJJ6IiAapQ+JJV1tERHRUWjwREQ3SO/EbPGnxREREZ6XFExHRIHV4xpPEExHRIHVIPI3tapN0naTFkm4pt0tbrs2WdGe5zZO0T8u1gyUtkHSrpEWS3tudO4iIaKZGtXgkrQGsbvvx8tRRtuf3K3Mw8F5gH9sPStoNuEzSXsBDwBxgL9vLJK0JbFV+bgPbyzt1LxERo1GDiQua0eKRtIOkLwOLge2HKf5/gRNsPwhg+2bgW8D7gekUyfih8tqTtheXnztc0h2S/kHSJu24j4iIyaC2iUfSupKOlfRL4GxgEbCz7QUtxS5o6Wr7UnluJ+CmftXNB3ay/TAwF1gq6UJJR0maAmD7G8DrgXWAn0u6VNKBfdcjIiaCXlx565Y6d7XdD9wGvNv2nYOUeU5X23Bsv1vSy4D9gY8ArwOOKa/dC3xa0mcoktC5FEnrkP71SJoNzAbYaMMNmT5t2kjCiIhorDr/3/phwH3Af0r6hKQZFT+3CNi937ndgYV9B7Zvt/0ViqTzltaC5bOgM4GvAhcDHxvoS2zPsb2H7T2SdCKiUzyCrVtqm3hsX237cOBVwKPA5ZJ+ImmrYT56KvBFSRsBSJpF0aI5U9I0Sfu2lJ0FLC3LHSDpNuAzwLXAjraPt72QiIgJIl1tHWD7IeB04PSyNdLTcvkCSU+U+w/a3t/2XEmbA7+WZOAx4Gjb90uaDnxU0lnAE8DjlN1sFAMO3mB7aQduKyKisWqfeFrZnteyv+8Q5b4OfH2A848BBw3ymf4DEiIiJpwajKaub1dbRETUU6NaPBERk10dWjxJPBERDZK52iIiIvpJiyciokEmfnsnLZ6IiOiwtHgiIhokLZ6IiOio8Z4yp5wMebGkJZJOHOD6mpIuKq/fUGH2mCSeiIgYmKSpwBkUkyLvCBwpacd+xY4DltveDvgK8MXh6k3iiYhokHFu8ewFLLF9l+2VwHeAQ/uVOZRiTTOAS4H9JGmoSvOMpwPuXrp0yL+E8SRptu05nfq+TujUPd3T7i/oJ39X9VGn+7pnBL9vWpdvKc3pd5+bA/e2HC8DXt6vmqfL2F4l6VFgI+DBwb43LZ7mmT18kdpp4j1BM++rifcEDb2v1uVbyq0jyTWJJyIiBnMfsGXL8RbluQHLSFoNWI9iNv9BJfFERMRgbgRmStpa0hrAEcDcfmXmAu8s9w8Dfmp7yEdIecbTPLXohx6hJt4TNPO+mnhP0Nz7GlL5zOYDwFXAVOBc2wslnQLMtz0XOAf4tqQlwMMUyWlIGiYxRUREjKt0tUVEREcl8UREREcl8UREREcl8UREREcl8dScpFMlPU/S6pKukfQnSUd3O66xkvSh8r4k6RxJN0s6oNtxjUUT7wlA0vblv707yuOdJZ3U7bjaQdILuh1DEyTx1N8Btv8MHEwx68t2wAldjWh8vKu8rwOADYC3A1/obkhj1sR7Ajgb+BjwFIDt26gwpLamzul2AE2QxFN/fe9i/TVwie1HuxnMOOqbb+og4Nu2F7acq6sm3hPAOrbn9Tu3qiuRtJntv+52DE2QxFN/P5B0J7A7cI2kTYD/6XJM4+EmSVdT/JK+StJ0oLfLMY1VE+8J4EFJ21JOeCzpMOD+7oYUE1leIG0ASRsCj9rukbQO8Dzbf+x2XGMhaQowC7jL9iOSNgI2L7txaqmJ9wQgaRuKN/tfASwH7gaOsr20q4HFhJUpc2pO0jta9lsvnd/5aMaVKRaeOhg4BVgXWKurEY2dgZ2AA4HPAdOo8T1J+nDL4RXAtRS9KI8DbwFO60ZcMfEl8dTfni37awH7ATdT/8RzJkU31GspEs9jwHd59v3WzZkUz3R2o0g8j1EsnFXXe5pe/nwxxT1cTnF/bwf6P/OJeFoST83Z/mDrsaT1KVYJrLuX295N0gIA28vL2XFrR9Khti8H9ra9q6RrAWw/XNd7ArD9KQBJPwd2s/1YeXwy8MMuhhYTXAYXNM/jwNbdDmIcPFWu9973wHoTavggXtIhwC7l4com3NMAng+sbDleWZ6LGFBaPDUn6fs8s3z6VGAH4OLuRTRuvgp8D9hU0mcp1vn4p+6GNCq/LaeOh+KeLgO2lPQ5iucgTXjR8nxgnqTvlcdvBM7rXjgx0WVUW81Jek3L4Spgqe1l3YpnPEl6CcUzKwHX2P5tl0MasybeE4Ck3YBXlYc/t72gm/HExJbE0wCSns8zD6jn2X6gm/GMB0nftv324c7VRdnFttD2S7odS0S35RlPzUl6G8UIorcCbwNuKF/gq7udWg/Ktdx371IsY2a7B1gs6UXdjiWi2/KMp/7+Edizr5VTPrD+CcUw3dqR9DHg48Dakv7cd5rigXXdlx/eAFgoaR7FIBAAbB/SvZAiOi+Jp/6m9Otae4gat2Rtfx74vKRTgduBbWx/qmwp1H1m4LUoXojtI+CLXYolomuSeOrvSklXAReWx4dTvEVeS5JeY/tnwPOAvSleIP0UzXiBdLXy3p4mae1uBRPRLUk89fcA8O8Uc4ABzLH9vSHKT1iS3gxsC/wM2KtBL5D+HfA+YBtJrfOyTQd+1Z2oIroniaf+1gVOBB4GLgJ+3d1wxuSPwP8q9xvxAmnpP4Argc9T/F31ecz2w90JKaJ7Mpy6ISTtTNHN9hZgme39uxzSqEjayvY9ko6iuJ/dgG9RvEB6ku1LuhpgRIxZWjzN8QBFi+EhYNMuxzJqtu8pf14g6SaeednyjU152TJiskuLp+YkvY/i/Z1NgEuAi20v6m5UERGDS4un/rYEjrd9S7cDiYioIi2eiIjoqNq+aBgREfWUxBMRER2VZzzROJI2Aq4pD18A9AB/Ko/3sr1ywA+O7Tt3Aza1/aMBrk0Dvkkx8amA5cD/tv2XUXzPm4FFtu8cY8gRXZPEE41j+yHKmRzKZZhX2P7nqp+XNLWcTXokdgNeCjwn8QB/D/yX7SPK+l8CPDXC+vu8meJF2iSeqK10tcWkIun7km6StFDSu8tzq0l6RNK/lFPa7CXpEEmLy7Jfk3RZWXaapPMkzZO0QNIbyvnWPgEcJemWAZal2Ay4r+/A9p22nyrre2dZ1y2SzpQ0pSWeL0i6VdJvJG0q6VXAQcBXyvJbSZop6aoyzp9L2r6s998lnS7p15LukvSmlj+Dj0u6vaz7s+W5AeuJaAvb2bI1dgNOBj7Scrxh+XMdYBHFUgWrUUzN8+aWa8uAGRRdY5cAl5XXTgWOKPc3AH5HMev0u4F/GSSG3Sm6+n4NfBrYrjz/UoqlsFcrj+cAf9MSz+vL86cBJ5b7/07xMm1f3dcC25b7rwSubil3YRn/zsCd5fk3AL8A1u735zFgPdmytWNLV1tMNn8vqW/9my0oJiW9hWK9n77JVXcEFtteCiDpQuAd5bUDgNdL6ptzbS1gyMXdbN8kaZvys/sD8yXtVe7vWR4DrA3cW37sCdtXlvs38cyy0k+TtD7FDN7fLT8Pz+4+v8y2gdskbV6e2x841/bYYTPAAAABjklEQVQTZWwPV6gnYlzlH1dMGpL2B14N7G37CUm/pEgcUPyir/JSW9/0PX/oV/erh/qQ7b5lHb6r4rf768u6zrX9T/3qWo0iEfbpYeD/VgU8aHvWANcAnuxXdjDD1RMxrvKMJyaT9YCHy6SzE4Ov7bMIeLGkLcskcXjLtauAD/YdSNq13H2MYpmD55C0T9mqQNKawA7AUoqVYt8maePy2kYVlsZ++ntsLwfu73t+Uz4f2mWYz/8YeFffOkCSNhxlPRGjlsQTk8kPgXUkLQI+A9wwUCEXw5w/QJEY5gOPAI+Wlz8FrFs+nF9I8QwJ4KfALuWAg/6DC2YCv5B0O3Az8Bvgctu3l/X9pBzUcDXw/GHu4ULg432DC4AjgL+VdCuwkGevcDrQvf2AYuTdfEm3UIy4Y6T1RIxFpsyJGICkabZXlC2es4DbbX+t23FFNEFaPBED+7uyRbCI4qH/2V2OJ6Ix0uKJiIiOSosnIiI6KoknIiI6KoknIiI6KoknIiI6KoknIiI6KoknIiI66v8DiYrlLFqNbFQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAAEUCAYAAACPsflDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAHpVJREFUeJzt3XmYXVWd7vHvSxAZQjOIAwImCEEJiAgh0gzKFbQBNahwBcQBBEJ3C1ebBkWbRhrsbsUrttKIBOXSKBdkUIwSDbMogkmYEhIIhkAkaWxlFJAmpOrtP/YOHIoadlWdU2d6P8+zn5y19zrr/HYF8qu19jpryTYRERGtZI1mBxAREdFXklNERLScJKeIiGg5SU4REdFykpwiIqLlJDlFRETLSXKKiIgRk3S+pD9IunuA65L0TUlLJM2XtFOVdpOcIiJiNC4A9h3k+n7ApPKYDpxTpdEkp4iIGDHbNwGPDVLlAOBCF24FNpS06VDtJjlFREQjbQY8VFNeXp4b1JoNCyeGZeKECR23jtQDV/5rs0Oou5uPrTQi0XY23GBls0Oouw8t/K9mh9AQ9y17UKNuZOEVlf+90fYHHUMxHLfaDNszRh3DEJKcIiJiQGUiGk0yWgFsUVPevDw3qAzrRUR0Gff0VD7qYCbw8XLW3q7Ak7YfHupN6TlFRMSISboY2AvYRNJy4IvAKwBsfxuYBewPLAH+DBxRpd0kp4iIbtOzqm5N2T50iOsGPjXcdpOcIiK6jHurJ6fRz74YmTxzioiIlpOeU0REt6nPRIeGSs8pIiJaTnpOERFdxnWcENEoSU4REd2mDZJThvUiIqLlpOcUEdFlhjOVvFnSc4qIiJbTtclJ0o2SFku6U9I9kqbXXNtA0oXlzo33l683KK+tUe7qeLekBZLmStqyeXcSETFMPT3VjybpquQkaS1J69WcOsz2jsDuwFckrVWe/y6w1PbWtrcCHgC+U147GHg9sIPttwAfBJ4o299oLO4jImI03LOq8tEsXZGcJG0r6WvAYmCbfqqMB54BeiRtDewMnF5z/TRgiqStgE2Bh233Athebvvxst6VkmZKmiYpz/MiIkaoY5OTpPUkHSHpV8B5wCKK3s4dNdUukjSfImmdbrsHmAzcWb4GoHx9J7AdcCnw/nI48GuS3lbT3l7AmcBBwD2S/qVMdhERraNnVfWjSTo2OQEPA0cCR9new/Z3bT/Vp85htncA3gCcIGnCUI3aXg68Cfg80AtcJ2nv8ppt32j74xS9LwP3Sjqwv7YkTZc0T9K8p55+eqT3GRHRcTo5OR1EsdviDyWdMljisf1H4Hbg7RQ9rB0lvfCzKV/vWF7D9nO2f2b7ROBfgA/U1F1H0keAHwJ/BXwauGaAz51he4rtKeuPHz+6u42IqMi9PZWPZunY5GT7atsHA3sCTwI/lnStpIl960paF3gbcL/tJcAdwMk1VU4Gbre9RNJOkl5fvm8NYAdgWVk+gyKB7QacWCaes23/qVH3GRExXO0wIaLjH9rbfhT4BvANSVOB2l8FLpL0LPBK4ALbt5XnjwTOknR/Wb6lPAfwGuA8Sa8sy3OAfy9f3wicYvu/G3IzERFdouOTUy3bc2pe7zVIvceBjw5w7efAzwe4NmuUIUZENF7W1ouIiBi+ruo5RUQETZ3oUFV6ThER0XLSc4qI6DZt8MwpySkiosu0w064GdaLiIiWk55TRES3Sc8pIiJi+NJziojoMu0wlTzJKSKi22RYLyIiYvjSc4qI6DLuybBeVPTgrDObHULdbbn/8c0Ooe7c7AAaZXmzA4h4qSSniIgu0w5fwk1yiojoNr2tn5wyISIiIlpOek4REV2mHSZEpOcUERGjImlfSYslLZF0Uj/X3yDpBkl3SJovaf+h2kzPKSKi29Sx5yRpHHA28G6KeZ9zJc20vaim2snApbbPkTQZmAVMHKzd9JwiImI0pgJLbC+1vRK4BDigTx0Df1G+3gD4z6EaTc8pIqLLDGcquaTpwPSaUzNsz6gpbwY8VFNeDry9TzOnAldLOg5YD9hnqM9NcoqI6DbDGNYrE9GMISsO7lDgAttfk/SXwPckbW+7d6A3ZFgvIiJGYwWwRU158/JcrSOBSwFs3wKsDWwyWKNJThERXcY9PZWPCuYCkyRtKWkt4BBgZp86vwP2BpC0LUVy+uNgjSY5RUTEiNleBRwLzAbuoZiVt1DSaZKmldX+Hjha0l3AxcDhtgddqjLPnCIiuky9Nxu0PYtienjtuVNqXi8Cdh9Om0lOERHdJitEREREDF96TmNA0q9t79bsOCIiIGvrRSmJKSJieNJzGgOSnrY9vtlxREQAuGfA7762jPScmkjSdEnzJM2bcdk1zQ4nIrpFT2/1o0nSc2qilywLsvCKQef8R0R0kySniIgukwkRERERI5CeU0REl3FP6z9FSM9pDGSmXkTE8KTnFBHRZdphKnmSU0REl2mH5JRhvYiIaDnpOUVEdBn3ZkJERETEsKXnFBHRZdphKnmSU0REl3HrLxCRYb2IiGg9QyYnSetK+kdJ55XlSZLe1/jQIiKiEdzjykezVOk5/T/gOeAvy/IK4EsNiygiIrpeleS0le0zgOcBbP8ZUEOjioiIhuntrX40S5UJESslrQMYQNJWFD2piIhoQ+0wIaJKcvoi8HNgC0kXAbsDhzcyqG40cf/jmx1CRETLGDI52b5G0u3ArhTDeZ+2/UjDI4uIiIZoh55Tldl6HwRW2b7K9k+BVZI+0PjQIiKiW1WZEPFF20+uLth+gmKoLyIi2lA7TIiokpz6q5OVJSIiomGqJJl5ks4Ezi7LnwJua1xIERHRSB3xzAk4DlgJ/KA8nqNIUBER0YZ6e1X5aJYqs/WeAU4ag1giIiKACslJ0jbACcDE2vq239W4sCIiolGaOdGhqirPnC4Dvg18B2iDkcqIiGh3VZLTKtvnNDySiIgYE50yIeInkv5W0qaSNl59NDyyiIhoiHpPiJC0r6TFkpZI6neOgqQPS1okaaGk/z9Um1V6Tp8o/zyx5pyBN1Z4b0REdDBJ4yi+avRuYDkwV9JM24tq6kwCPg/sbvtxSa8Zqt0qs/W2HHnYERHRanrrO6w3FVhieymApEuAA4BFNXWOBs62/TiA7T8M1WjVnXBPljSjLGcn3IiIWG0z4KGa8vLyXK1tgG0k3SzpVkn7DtVo1Z1wVwK7leXshFuS9GCzY4iIGK7hPHOSNF3SvJpj+gg+ck1gErAXcChwnqQNh3rDULayfbCkQ6HYCVdSdsKNiGhTHsbKD7ZnADMGqbIC2KKmvHl5rtZy4De2nwcekHQfRbKaO1CjVXpO2Ql3YH8EkLSXpF9I+rGkpZK+LOkwSXMkLSh/ZhERnWguMEnSlpLWAg4BZvapcyVFrwlJm1AM8y0drNEqyelUXroT7nXA54YTeaeyvUtN8a3AXwPbAh8DtrE9leLLy8f19/7a7vJTTz/d8HgjIqC+W2bYXgUcC8wG7gEutb1Q0mmSppXVZgOPSloE3ACcaPvRwdqtMlvvakm3kZ1whzLX9sMAku4Hri7PLwD+V39vqO0uT5wwwWMRZEREvdmeBczqc+6UmtcGji+PSqqsrXed7b2Bq/o5Fy+qHersrSn3kv2vIqKFNHO18aoG/EdT0trAusAmkjai6DUB/AUvnyYYERFRN4P9Rn8M8Bng9RSbC65OTn8C/r3BcUVERIO0Q89JxVDgIBWk42yfNUbxdK08c4qIKh5ctmzUmWXOXrtX/vdm6o03NyWTVZkQcZak3Xj5fk4XNjCuiIjoYlUmRHwP2Aq4kxf3czKQ5BQR0YbaYVivyiyyKcBkDzX+FxERUSdVktPdwOuAhxscS0REjIFed0bPaRNgkaQ51HyXx/a0gd8SERGtqsrKD81WJTmd2uggIiIialWZrfcLSROASbavlbQuMK7xoUVERCP0tMGwXpXNBo8GLgfOLU9tRrHCbERERENUGdb7FMU2vL8BsP3bKvu/R0REa+qUqeTP2V65en9BSWtS7u0UERHtpyOG9YBfSPoCsI6kdwOXAT9pbFgREdHNqiSnkyh2fF1AsRjsLODkRgYVERGN02tVPpqlymy9XuA8Sf8BbAesyGoREdHqqvzmHa1rwL8/Sd+WtF35egOKtfUuBO6QdOgYxRcREXXWY1U+mmWwXy72tL2wfH0EcJ/ttwA7A59teGQREdG1BhvWW1nzevVECGz/fvXMvYiIaD89bfBgZrDk9ISk9wErgN2BI+GFqeTrjEFsERHRAO2+8OsxwDcpViT/jO3fl+f3Bq5qdGAREdG9BkxOtu8D9u3n/GxgdiODioiIxumUL+FGRESMqSrLF0VERAdp9wkRERHRgXrogGE9Sa+V9F1JPyvLkyUd2fjQIiKiW1V55nQBxQSI15fl+4DPNCqgiIhorB5XP5qlSnLaxPalQC+A7VVAT0OjioiIrlblmdMzkl5FuYeTpF2BJxsaVURENEw79C6qJKfjgZnAVpJuBl4NHNTQqCIiomHaITkNOaxn+3bgncBuFKtGbGd7fqMDqwdJe0paKOlOSU1ZcknSjZKmNOOzIyLaVZXZep8CxtteaPtuYLykv218aNWoMNB9HAb8q+0dbT9boa1MrY+IjteDKh/NUmVCxNG2n1hdsP04cHTjQhqapImSFku6ELgb+JikWyTdLukySeMlHQV8GDhd0kVlEvuqpLslLZB0cNnWXpJ+KWkmsKhs+15JF0i6r3zvPpJulvRbSVPL960n6XxJcyTdIemA8vw6ki6RdI+kH5FFciMihq1KT2GcJK3e/VbSOGCtxoZVySTgE8AS4IfAPrafkfQ54Hjbp0naA/ip7cslHQjsCLwV2ASYK+mmsq2dgO1tPyBpIrA18L+BTwJzgY8AewDTgC8AHwD+Abje9iclbQjMkXQtxdDnn21vK2kH4PaBbkDSdGA6wMYbb8z648fX62cTETGgnjbYzLxKz2k28ANJe0vaG7gY+Hljw6pkme1bgV2BycDNku6kSFgT+qm/B3Cx7R7b/wX8AtilvDbH9gM1dR+wvaDcon4hcF2ZnBcAE8s67wFOKj/zRmBt4A3AO4DvA5TP5gZ8Pmd7hu0ptqckMUVEu5K0bzmatUTSSYPUO1CSqzyHr9Jz+izFb/d/U5avAb5TKeLGeqb8U8A1tkezdfwzfcrP1bzurSn38uLPTMCBthfXvjEbMUZEq6vnbL1yNO1sik1pl1OMSs20vahPvfWBTwO/qdLuoD2n8kO/Z/vbtg8qj3Ntt9JMxFuB3SVtDS88C9qmn3q/BA6WNE7Sqyl6OHNG8bmzgeNUZiNJbyvP30QxDIik7YEdRvEZERF11zOMo4KpwBLbS22vBC4BDuin3unAV4D/rtLooMmpTEITJLXCM6Z+2f4jcDhwsaT5wC3Am/up+iOKIba7gOuBz9ZsoDgSpwOvAOZLWliWAc6hmNF4D3AacNsoPiMioqkkTZc0r+aY3qfKZsBDNeXl5bnaNnYCtrBdeaPaKsN6Syme58ykZvjL9plVP6TebD8IbF9Tvp4Xnx/V1ju85rWBE8ujts6NFM+MBmr78P6ulVPTj+nnM58FDhnO/UREjKXhDH3ZngHMGOlnlV/1OZOiE1FZleR0f3msAaw/7MgiIqKTrQC2qClvXp5bbX2KX+pvLJ+CvA6YKWma7XkDNTpkcrL9TyMKNyIiWlIPdZ1KPheYJGlLiqR0COVzdwDbT1J8fQcoVs0BThgsMUGF5CTpBnj5ndh+V9XIIyKiddRzRpvtVZKOpZgkNg443/ZCSacB82zPHEm7VYb1Tqh5vTZwILBqJB8WERGdx/YsYFafc6cMUHevKm1WGdbrO9vsZkmjmYIdERFN1A4rRFQZ1tu4prgGsDOwQcMiioiIrldlWO82imdOohjOewA4spFBRURE47TSKgoDqTKst+VYBBIREWOjzrP1GqLKsN4rKNbVe0d56kbgXNvPNzCuiIjoYlWG9c6hWKbnW2X5Y+W5oxoVVERENE5H9JyAXWy/taZ8vaS7GhVQRERElf2ceiRttbog6Y20x/O0iIjoR51XJW+IKj2nE4EbJC2lmLE3ATiioVFFR+jEna2+uvGkZofQEL/rbXYE9XfcGfs0O4QYhSqz9a6TNAl4U3lqse3nBntPRES0rrb+Eq6kXYCHbP/e9nOSdqRYumiZpFNtPzZmUUZERN20w4SIwZ45nQusBJD0DuDLwIXAk4xib4+IiIihDDasN66md3QwMMP2FcAVku5sfGgREdEI7d5zGidpdfLam2Jr89WqTKSIiIgYkcGSzMXALyQ9AjwL/BJA0tYUQ3sREdGGett5QoTtf5Z0HbApcLX9wt2sARw3FsFFRET9tcOw3qDDc7Zv7efcfY0LJyIiIs+OIiK6Tjv0nKosXxQRETGm0nOKiOgybb1CREREdKYM60VERIxAek4REV2mHb7nlJ5TRES0nPScIiK6TDs8c0pyiojoMu2QnDKsFxERLSfJqYkkTZc0T9K8p55+utnhRESX6LUrH82S5NREtmfYnmJ7yvrjxzc7nIiIlpFnThERXSbPnAJJsyS9vtlxRES0k/ScGsz2/s2OISKiVtbWi4iIltObYb2IiIjhS3KKiOgyPXblowpJ+0paLGmJpJP6uX68pEWS5ku6TtKEodpMcoqIiBGTNA44G9gPmAwcKmlyn2p3AFNs7wBcDpwxVLtJThERXabOX8KdCiyxvdT2SuAS4IDaCrZvsP3nsngrsPlQjWZCREREl6nz95w2Ax6qKS8H3j5I/SOBnw3VaJJTREQMSNJ0YHrNqRm2Z4ywrY8CU4B3DlU3ySkiosv0urdy3TIRDZaMVgBb1JQ3L8+9hKR9gH8A3mn7uaE+N8+cIiJiNOYCkyRtKWkt4BBgZm0FSW8DzgWm2f5DlUbTc4qI6DL1/BKu7VWSjgVmA+OA820vlHQaMM/2TOCrwHjgMkkAv7M9bbB2k5wiIrpMvZcvsj0LmNXn3Ck1r/cZbpsZ1ouIiJYjt8ECgN1g4oQJ+YuIplGzA2iATrwngKXLlo361t619eTK/95cv2RRU36U6TlFRETLyTOniIgu08zt16tKzykiIlpOek4REV2m+ldwmyfJKSKiy2RYLyIiYgTSc4qI6DLZpj0iImIE0nOKiOgy7fDMKckpIqLLZFgvIiJiBNJziojoMuk5RUREjEB6ThERXaa39TtOSU4REd0mw3otTtKNkhZLurM8Lq+5Nl3SveUxR9IeNdfeJ+kOSXdJWiTpmObcQUREZ+q6npOktYBX2H6mPHWY7Xl96rwPOAbYw/YjknYCrpQ0FXgUmAFMtb1c0iuBieX7NrL9+FjdS0TESKTn1EIkbSvpa8BiYJshqn8OONH2IwC2bwf+A/gUsD5FUn+0vPac7cXl+w6WdLekv5f06kbcR0REN+jo5CRpPUlHSPoVcB6wCNjB9h011S6qGdb7anluO+C2Ps3NA7az/RgwE1gm6WJJh0laA8D2t4H9gHWBmyRdLmnf1df7iW+6pHmS5j319NN1u++IiMHY1Y9m6fRhvYeB+cBRtu8doM7LhvWGYvsoSW8B9gFOAN4NHF5eewg4XdKXKBLV+RSJbVo/7cygGCJk4oQJrd/PjogYIx3dcwIOAlYAP5R0iqQJFd+3CNi5z7mdgYWrC7YX2P46RWI6sLZi+WzqW8A3gUuBz48s/IiI+uvFlY9m6ejkZPtq2wcDewJPAj+WdK2kiUO89QzgK5JeBSBpR4qe0bckjZe0V03dHYFlZb33SJoPfAm4AZhs+zO2FxIR0SI8jKNZOn1YDwDbjwLfAL5R9mp6ai5fJOnZ8vUjtvexPVPSZsCvJRl4Cvio7YclrQ98VtK5wLPAM5RDehSTJN5ve9kY3FZERMeS22Dp9G6QZ07RTGp2AA3QifcEsHTZslHf2psnTKz87829yx5syo+yo4f1IiKiPXXFsF5ERLyoHYZpkpwiIrpMOySnDOtFRETLSc8pIqLLZG29iIiIEUjPKSKiy7R+vynJKSKi67RDcsqwXkREtJwkp4iILlPvtfXKrYEWS1oi6aR+rr9S0g/K67+psL5pklNERIycpHHA2RRbBE0GDpU0uU+1I4HHbW8NfB34ylDtJjlFRHSZOvecpgJLbC+1vRK4BDigT50DKHYTB7gc2FvSoGv2ZUJEi3iwDos5ViFpernJYUfpxPvqxHuC3FcrGM6/N5KmA9NrTs3oc5+bAQ/VlJcDb+/TzAt1bK+S9CTwKuCRgT43PafuM33oKm2pE++rE+8Jcl9txfYM21NqjjFJwElOERExGiuALWrKm5fn+q0jaU1gA4r97waU5BQREaMxF5gkaUtJawGHADP71JkJfKJ8fRBwvYfYTDDPnLpPW4yJj0An3lcn3hPkvjpK+QzpWGA2MA443/ZCSacB82zPBL4LfE/SEuAxigQ2qOyEGxERLSfDehER0XKSnCIiouUkOUVERMtJcuoCkvaQdET5+tWStmx2TPUkaSNJOzQ7joionySnDifpi8DngM+Xp14BfL95EdWHpBsl/YWkjYHbgfMkndnsuEZL0jaSrpN0d1neQdLJzY6rESS9rtkxNEKn3tdYS3LqfB8EpgHPANj+T2D9pkZUHxvY/hPwIeBC228H9mlyTPVwHsUvEs8D2J5PhWm3beq7zQ6gQTr1vsZUklPnW1l+2c0AktZrcjz1sqakTYEPAz9tdjB1tK7tOX3OrWpKJA1m+73NjqEROvW+xlqSU+e7VNK5wIaSjgauA77T5Jjq4TSKL/0tsT1X0huB3zY5pnp4RNJWvPjLxEHAw80NKWLs5Uu4XUDSu4H3lMXZtq9tZjwxsDLJzgB2Ax4HHgAOs72sqYFFjLEkpw4l6Ve295D0FMVv4bVL5PdSLCHyVdvfakqAIyTps7bPkHQW/Ww3Y/v/NCGsUZN0fJ9T61CMbKx+Vtj2kz0ihiNr63Uo23uUf/Y7+UHSq4BfA22VnIB7yj/nNTWK+lv99/QmYBfgxxS/UHwM6PsMKqLjpefUxSRtajvPM1qIpJuA99p+qiyvD1xl+x3NjSxibKXn1MXaOTFJuoH+h/Xe1YRw6um1wMqa8sryXERXSXKKdnVCzeu1gQPpjCnXFwJzJP2oLH8AuKB54UQ0R4b1omNImmN7arPjGC1JOwF7lsWbbN/RzHgimiE9p2hL5bJFq60BTKHY+rnt2b6dYkmmiK6V5BTt6jZenCL/PPAgcGQzA4qI+skKEdGuPgfsaHtL4HsU3wf6c3NDioh6SXKKdnWy7T9J2gN4F8WSTOc0OaaIqJMkp2hXPeWf7wXOs30VsFYT44mIOkpyina1olzQ9mBglqRXkv+eIzpGppJHW5K0LrAvsMD2b8vtM95i++omhxYRdZDkFBERLSfDIBER0XKSnCIiouXkS7jRlcotQ64ri6+jmP33x7I81fbKft84us/cCXiN7Z/3c208xXT47Si+WPw48Fe2h/3dLUkfAhbZvneUIUc0TZJTdCXbjwI7Akg6FXja9v+t+n5J42z3DF3zJXYCtgdelpyAvwN+Z/uQsv03U6x8MRIfothQMskp2laG9SL6kPQTSbdJWijpqPLcmpKekPRvkuYDUyVNk7S4rHuWpCvLuuMlXSBpjqQ7JL1f0jrAKcBhku6UdFCfj90UWLG6YPte28+X7X2ibOtOSd+StEZNPF+WdJekWyS9RtKewP7A18v6EyVNkjS7jPMmSduU7X5f0jck/VrSUkkfrPkZfEHSgrLtfy7P9dtOREPYzpGjqw/gVOCEmvLG5Z/rAouAjShGGQx8qObacmACxTDcZcCV5bUzgEPK1xsB91Fs63EU8G8DxLAzxbDir4HTga3L89sDVwJrluUZwEdq4tmvPH8mcFL5+vvAB2ravgHYqny9O3B1Tb2Ly/h3AO4tz78f+CWwTp+fR7/t5MjRiCPDehEv93eSppWvNwe2Au6k2Phv9T5Lk4HFtpcBSLoY+Hh57T3AfpJOKstrA28Y7ANt3ybpjeV79wHmSZpavt6lLAOsAzxUvu1Z2z8rX9/Gi9tsvEDShsCuwBXl++Glw/lX2jYwX9Jm5bl9gPNtP1vG9liFdiLqKv9xRdSQtA/wDmBX289K+hVFcoEiGVT5YqAoei7392l70K3WXWzNfgVFAhCwX9nW+bb/sU9ba/LSHXN76P//ZwGP2N5xgI99rk/dgQzVTkRd5ZlTxEttADxWJqbtKHot/VkEvEnSFmUiObjm2mzguNUFSW8rXz4FrN9fY5L2KHsnlEsxbQssA64FPixpk/LaqyQN2gur/RzbjwMPr36eVD6veusQ778G+GT5nAxJG4+wnYgRS3KKeKmrgHUlLQK+BPymv0oupngfS5E85gFPAE+Wl/8JWK+cULCQ4pkWwPXAW8tJEn0nREwCfilpAcVGg7cAP7a9oGzv2nIixtXAa4e4h4uBL6yeEAEcAvy1pLuAhcD7Bnuz7Z9SzCicJ+lOipmEDLediNHI8kURIyRpvO2ny57TuRTr/J3V7LgiOkF6ThEj9zdlz2IRxUSF85ocT0THSM8pIiJaTnpOERHRcpKcIiKi5SQ5RUREy0lyioiIlpPkFBERLSfJKSIiWs7/AGxAOgcDpGLUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sample in top_results:\n",
    "    plt.figure()\n",
    "    target_len = len(sample['sampled'])\n",
    "    source_len = len(sample['source'])\n",
    "\n",
    "    attention_matrix = sample['attention'][:target_len, :source_len+2].transpose()#[::-1]\n",
    "    ax = sns.heatmap(attention_matrix, center=0.0)\n",
    "    ylabs = [\"<BOS>\"]+sample['source']+[\"<EOS>\"]\n",
    "    #ylabs = sample['source']\n",
    "    #ylabs = ylabs[::-1]\n",
    "    ax.set_yticklabels(ylabs, rotation=0)\n",
    "    ax.set_xticklabels(sample['sampled'], rotation=90)\n",
    "    ax.set_xlabel(\"Target Sentence\")\n",
    "    ax.set_ylabel(\"Source Sentence\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'source': \"i 'm not going to tell you how to do that .\",\n",
       " 'truth': 'je ne vais pas te dire comment le faire .',\n",
       " 'sampled': 'je suis suis pas de de de . . .'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_source_sentence(vectorizer, batch_dict, index):\n",
    "    indices = batch_dict['x_source'][index].cpu().data.numpy()\n",
    "    vocab = vectorizer.source_vocab\n",
    "    return sentence_from_indices(indices, vocab)\n",
    "\n",
    "def get_true_sentence(vectorizer, batch_dict, index):\n",
    "    return sentence_from_indices(batch_dict['y_target'].cpu().data.numpy()[index], vectorizer.target_vocab)\n",
    "    \n",
    "def get_sampled_sentence(vectorizer, batch_dict, index):\n",
    "    y_pred = model(x_source=batch_dict['x_source'], \n",
    "                   x_source_lengths=batch_dict['x_source_length'], \n",
    "                   target_sequence=batch_dict['x_target'])\n",
    "    return sentence_from_indices(torch.max(y_pred, dim=2)[1].cpu().data.numpy()[index], vectorizer.target_vocab)\n",
    "\n",
    "def get_all_sentences(vectorizer, batch_dict, index):\n",
    "    return {\"source\": get_source_sentence(vectorizer, batch_dict, index), \n",
    "            \"truth\": get_true_sentence(vectorizer, batch_dict, index), \n",
    "            \"sampled\": get_sampled_sentence(vectorizer, batch_dict, index)}\n",
    "    \n",
    "def sentence_from_indices(indices, vocab, strict=True):\n",
    "    ignore_indices = set([vocab.mask_index, vocab.begin_seq_index, vocab.end_seq_index])\n",
    "    out = []\n",
    "    for index in indices:\n",
    "        if index == vocab.begin_seq_index and strict:\n",
    "            continue\n",
    "        elif index == vocab.end_seq_index and strict:\n",
    "            return \" \".join(out)\n",
    "        else:\n",
    "            out.append(vocab.lookup_index(index))\n",
    "    return \" \".join(out)\n",
    "\n",
    "results = get_all_sentences(vectorizer, batch_dict, 1)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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